diff --git a/R/CTS_Server.R b/R/CTS_Server.R
index d640a07..76ddc39 100644
--- a/R/CTS_Server.R
+++ b/R/CTS_Server.R
@@ -2827,9 +2827,9 @@ element}
 #'}
 #'@details This depends on the following UI values in the state
 #'\itemize{
-#'  \item{} state[["CTS"]][["ui"]][["covariate_value"]]
-#'  \item{} state[["CTS"]][["ui"]][["covariate_type_selected"]]
-#'  \item{} state[["CTS"]][["ui"]][["selected_covariate"]]
+#'  \item{} \code{state[["CTS"]][["ui"]][["covariate_value"]]}
+#'  \item{} \code{state[["CTS"]][["ui"]][["covariate_type_selected"]]}
+#'  \item{} \code{state[["CTS"]][["ui"]][["selected_covariate"]]}
 #'}
 #'@example inst/test_apps/CTS_funcs.R
 CTS_add_covariate   = function(state, element){
diff --git a/docs/404.html b/docs/404.html
index 6ee9532..8749d71 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -16,7 +16,7 @@
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 <meta property="og:image" content="/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -112,7 +112,7 @@
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 692bc7e..015ce53 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,5 +1,5 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -85,7 +85,7 @@
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 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/articles/clinical_trial_simulation.html b/docs/articles/clinical_trial_simulation.html
index 46c6194..fb30094 100644
--- a/docs/articles/clinical_trial_simulation.html
+++ b/docs/articles/clinical_trial_simulation.html
@@ -17,7 +17,7 @@
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Clinical Trial Simulation">
+<!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Clinical Trial Simulation">
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@@ -383,20 +383,20 @@ <h3 id="defining-subjects">Defining subjects<a class="anchor" aria-label="anchor
 <span><span class="co">## 4  4 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000</span></span>
 <span><span class="co">## 5  5 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000</span></span>
 <span><span class="co">## 6  6 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000</span></span>
-<span><span class="co">##   TV_kdeg_BM TV_Emax TV_EC50       ETAka      ETACL      ETAVc      ETAVp</span></span>
-<span><span class="co">## 1        0.1     100     300  1.21921403 -0.2009231 -0.3384823  0.1217864</span></span>
-<span><span class="co">## 2        0.1     100     300 -1.11242836 -0.1640987 -0.1598991  0.1466910</span></span>
-<span><span class="co">## 3        0.1     100     300 -0.06807962 -0.3341488 -0.5686558 -0.1215476</span></span>
-<span><span class="co">## 4        0.1     100     300 -0.44491868  0.6433960  0.2223175  0.5390626</span></span>
-<span><span class="co">## 5        0.1     100     300 -0.44409456 -0.4895307 -0.5000220 -0.1807666</span></span>
-<span><span class="co">## 6        0.1     100     300 -0.15464546 -0.4426699 -0.3959970 -0.3404295</span></span>
-<span><span class="co">##         ETAQ    ETABM_IC    ETAEmax SEX_ID SUBTYPE_ID       WT</span></span>
-<span><span class="co">## 1  0.3975154  0.23930808  0.1464248      1          0 53.65521</span></span>
-<span><span class="co">## 2  2.1129804  0.45798783 -0.1063589      0          0 88.42572</span></span>
-<span><span class="co">## 3 -0.8616051  0.36654199 -0.3259271      1          0 65.97745</span></span>
-<span><span class="co">## 4  0.5297385  0.27421551  0.1487639      1          0 58.48501</span></span>
-<span><span class="co">## 5 -0.5466695  0.17227432 -0.1335189      1          0 63.43319</span></span>
-<span><span class="co">## 6  1.3824647 -0.03320645 -0.2711974      1          0 82.05225</span></span></code></pre>
+<span><span class="co">##   TV_kdeg_BM TV_Emax TV_EC50       ETAka       ETACL       ETAVc       ETAVp</span></span>
+<span><span class="co">## 1        0.1     100     300  0.30065410  0.02638762  0.22298038 -0.03439826</span></span>
+<span><span class="co">## 2        0.1     100     300  0.08502272  0.09481141 -0.26303939  0.24911727</span></span>
+<span><span class="co">## 3        0.1     100     300 -1.12982057  0.32226445 -0.16893668  0.53718003</span></span>
+<span><span class="co">## 4        0.1     100     300  0.41441629 -0.12367437 -0.31273632 -0.07090379</span></span>
+<span><span class="co">## 5        0.1     100     300 -0.48897344  0.02604895 -0.03895797 -0.17078974</span></span>
+<span><span class="co">## 6        0.1     100     300  1.11064814 -0.65061266 -0.33562320 -0.35589967</span></span>
+<span><span class="co">##         ETAQ    ETABM_IC     ETAEmax SEX_ID SUBTYPE_ID       WT</span></span>
+<span><span class="co">## 1  0.6900363 -0.04931769  0.39039383      0          0 51.00773</span></span>
+<span><span class="co">## 2  1.4615575  0.32202532  0.75203484      0          0 81.35479</span></span>
+<span><span class="co">## 3 -0.4840149 -0.29372697 -0.04508853      1          0 70.17848</span></span>
+<span><span class="co">## 4 -2.7754122  0.25222657 -0.05102237      1          0 73.50821</span></span>
+<span><span class="co">## 5  0.2097416 -0.51675088  0.24546028      0          0 68.13168</span></span>
+<span><span class="co">## 6 -0.8491960 -0.20730296 -0.06938995      0          0 72.56017</span></span></code></pre>
 </div>
 <div class="section level3">
 <h3 id="creating-rules">Creating rules<a class="anchor" aria-label="anchor" href="#creating-rules"></a>
@@ -735,7 +735,7 @@ <h2 id="antibody-pk-and-biomarker-system">Antibody-PK and biomarker system<a cla
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer>
diff --git a/docs/articles/clinical_trial_simulation_files/figure-html/unnamed-chunk-17-1.png b/docs/articles/clinical_trial_simulation_files/figure-html/unnamed-chunk-17-1.png
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diff --git a/docs/articles/clinical_trial_simulation_files/figure-html/unnamed-chunk-26-2.png b/docs/articles/clinical_trial_simulation_files/figure-html/unnamed-chunk-26-2.png
index 3a0339b..b282188 100644
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diff --git a/docs/articles/deployment.html b/docs/articles/deployment.html
index 2e20f4f..1cc28a4 100644
--- a/docs/articles/deployment.html
+++ b/docs/articles/deployment.html
@@ -17,7 +17,7 @@
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Deployment and Customization">
+<!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Deployment and Customization">
 <meta property="og:description" content="ruminate">
 <meta property="og:image" content="/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -170,7 +170,9 @@ <h3 id="individual-modules">Individual Modules<a class="anchor" aria-label="anch
 <span><span class="va">UD.yaml</span>       <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"formods"</span>,  <span class="st">"templates"</span>,  <span class="st">"UD.yaml"</span><span class="op">)</span></span>
 <span><span class="va">DW.yaml</span>       <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"formods"</span>,  <span class="st">"templates"</span>,  <span class="st">"DW.yaml"</span><span class="op">)</span></span>
 <span><span class="va">FG.yaml</span>       <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"formods"</span>,  <span class="st">"templates"</span>,  <span class="st">"FG.yaml"</span><span class="op">)</span></span>
-<span><span class="va">NCA.yaml</span>      <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"ruminate"</span>, <span class="st">"templates"</span>,  <span class="st">"NCA.yaml"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">NCA.yaml</span>      <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"ruminate"</span>, <span class="st">"templates"</span>,  <span class="st">"NCA.yaml"</span><span class="op">)</span></span>
+<span><span class="va">MB.yaml</span>       <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"ruminate"</span>, <span class="st">"templates"</span>,  <span class="st">"MB.yaml"</span><span class="op">)</span></span>
+<span><span class="va">CTS.yaml</span>      <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span>package<span class="op">=</span><span class="st">"ruminate"</span>, <span class="st">"templates"</span>,  <span class="st">"CTS.yaml"</span><span class="op">)</span></span></code></pre></div>
 <p>Each of these controls different aspects of the module including the
 text displayed, icons used, certain behaviors of the app, default
 values, etc. These options and are described in the comments of the
@@ -201,7 +203,7 @@ <h3 id="individual-modules">Individual Modules<a class="anchor" aria-label="anch
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer>
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 4ee088a..9afe772 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -95,7 +95,7 @@ <h3>All vignettes</h3>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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index 63da329..a4beaa5 100644
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+++ b/docs/articles/noncompartmental_analysis.html
@@ -17,7 +17,7 @@
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 <script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Noncompartmental Analysis">
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@@ -98,6 +98,7 @@
 
 
 
+<link href="noncompartmental_analysis_files/htmltools-fill-0.5.8/fill.css" rel="stylesheet">
 <script src="noncompartmental_analysis_files/htmlwidgets-1.6.4/htmlwidgets.js"></script><link href="noncompartmental_analysis_files/datatables-css-0.0.0/datatables-crosstalk.css" rel="stylesheet">
 <script src="noncompartmental_analysis_files/datatables-binding-0.32/datatables.js"></script><link href="noncompartmental_analysis_files/dt-core-1.13.6/css/jquery.dataTables.min.css" rel="stylesheet">
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@@ -284,8 +285,8 @@ <h3 id="nca-parameters">NCA parameters<a class="anchor" aria-label="anchor" href
 <p>The following shows the different NCA parameters that can be
 calculated. The PKNCA parameter is value used in PKNCA, and the App
 Parameter Name is the value shown in the app.</p>
-<div class="datatables html-widget html-fill-item" id="htmlwidget-abef51c41afca6365d87" style="width:100%;height:auto;"></div>
-<script type="application/json" data-for="htmlwidget-abef51c41afca6365d87">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71","72","73","74","75","76","77","78","79","80","81","82","83","84","85","86","87","88","89","90","91","92","93","94","95","96","97","98","99","100","101","102","103","104","105","106","107","108","109","110","111","112","113","114","115"],["start","end","auclast","aucall","aumclast","aumcall","aucint.last","aucint.last.dose","aucint.all","aucint.all.dose","c0","cmax","cmin","tmax","tlast","tfirst","clast.obs","cl.last","cl.all","f","mrt.last","mrt.iv.last","vss.last","vss.iv.last","cav","ctrough","cstart","ptr","tlag","deg.fluc","swing","ceoi","aucabove.predose.all","aucabove.trough.all","ae","clr.last","clr.obs","clr.pred","fe","sparse_auclast","time_above","aucivlast","aucivall","aucivint.last","aucivint.all","aucivpbextlast","aucivpbextall","aucivpbextint.last","aucivpbextint.all","half.life","r.squared","adj.r.squared","lambda.z","lambda.z.time.first","lambda.z.n.points","clast.pred","span.ratio","thalf.eff.last","thalf.eff.iv.last","kel.last","kel.iv.last","aucinf.obs","aucinf.pred","aumcinf.obs","aumcinf.pred","aucint.inf.obs","aucint.inf.obs.dose","aucint.inf.pred","aucint.inf.pred.dose","aucivinf.obs","aucivinf.pred","aucivpbextinf.obs","aucivpbextinf.pred","aucpext.obs","aucpext.pred","cl.obs","cl.pred","mrt.obs","mrt.pred","mrt.iv.obs","mrt.iv.pred","mrt.md.obs","mrt.md.pred","vz.obs","vz.pred","vss.obs","vss.pred","vss.iv.obs","vss.iv.pred","vss.md.obs","vss.md.pred","vd.obs","vd.pred","thalf.eff.obs","thalf.eff.pred","thalf.eff.iv.obs","thalf.eff.iv.pred","kel.obs","kel.pred","kel.iv.obs","kel.iv.pred","auclast.dn","aucall.dn","aucinf.obs.dn","aucinf.pred.dn","aumclast.dn","aumcall.dn","aumcinf.obs.dn","aumcinf.pred.dn","cmax.dn","cmin.dn","clast.obs.dn","clast.pred.dn","cav.dn","ctrough.dn"],["time","time","auc","auc","aumc","aumc","auc","auc","auc","auc","conc","conc","conc","time","time","time","conc","clearance","clearance","fraction","time","time","volume","volume","conc","conc","conc","fraction","time","%","%","conc","auc","auc","amount","renal_clearance","renal_clearance","renal_clearance","fraction","auc","time","auc","auc","auc","auc","%","%","%","%","time","unitless","unitless","inverse_time","time","count","conc","fraction","time","time","inverse_time","inverse_time","auc","auc","aumc","aumc","auc","auc","auc","auc","auc","auc","%","%","%","%","clearance","clearance","time","time","time","time","time","time","volume","volume","volume","volume","volume","volume","volume","volume","volume","volume","time","time","time","time","inverse_time","inverse_time","inverse_time","inverse_time","auc_dosenorm","auc_dosenorm","auc_dosenorm","auc_dosenorm","aumc_dosenorm","aumc_dosenorm","aumc_dosenorm","aumc_dosenorm","conc_dosenorm","conc_dosenorm","conc_dosenorm","conc_dosenorm","conc_dosenorm","conc_dosenorm"],["Interval Start","Interval End","AUClast","AUCall","AUMC,last","AUMC,all","AUCint (based on AUClast extrapolation)","AUCint (based on AUClast extrapolation, dose-aware)","AUCint (based on AUCall extrapolation)","AUCint (based on AUCall extrapolation, dose-aware)","C0","Cmax","Cmin","Tmax","Tlast","Tfirst","Clast","CL (based on AUClast)","CL (based on AUCall)","Bioavailability","MRT (based on AUClast)","MRT (for IV dosing, based on AUClast)","Vss (based on AUClast)","Vss (for IV dosing, based on AUClast)","Cav","Ctrough","Cstart","Peak-to-trough ratio","Tlag","Degree of fluctuation","Swing","Ceoi","AUC,above","AUC,above","Amount excreted","Renal clearance (from AUClast)","Renal clearance (from AUCinf,obs)","Renal clearance (from AUCinf,pred)","Fraction excreted","Sparse AUClast","Time above Concentration","AUClast (IV dosing)","AUCall (IV dosing)","AUCint,last (IV dosing)","AUCint,all (IV dosing)","AUCbext (based on AUClast)","AUCbext (based on AUCall)","AUCbext (based on AUCint,last)","AUCbext (based on AUCint,all)","Half-life","$r^2$","$r^2_{adj}$","$\\lambda_z$","First time for $\\lambda_z$","Number of points used for lambda_z","Clast,pred","Span ratio","Effective half-life (based on MRT,last)","Effective half-life (for IV dosing, based on MRTlast)","Kel (based on AUClast)","Kel (for IV dosing, based on AUClast)","AUCinf,obs","AUCinf,pred","AUMC,inf,obs","AUMC,inf,pred","AUCint (based on AUCinf,obs extrapolation)","AUCint (based on AUCinf,obs extrapolation, dose-aware)","AUCint (based on AUCinf,pred extrapolation)","AUCint (based on AUCinf,pred extrapolation, dose-aware)","AUCinf,obs (IV dosing)","AUCinf,pred (IV dosing)","AUCbext (based on AUCinf,obs)","AUCbext (based on AUCinf,pred)","AUCpext (based on AUCinf,obs)","AUCpext (based on AUCinf,pred)","CL (based on AUCinf,obs)","CL (based on AUCinf,pred)","MRT (based on AUCinf,obs)","MRT (based on AUCinf,pred)","MRT (for IV dosing, based on AUCinf,obs)","MRT (for IV dosing, based on AUCinf,pred)","MRT (for multiple dosing, based on AUCinf,obs)","MRT (for multiple dosing, based on AUCinf,pred)","Vz (based on AUCinf,obs)","Vz (based on AUCinf,pred)","Vss (based on AUCinf,obs)","Vss (based on AUCinf,pred)","Vss (for IV dosing, based on AUCinf,obs)","Vss (for IV dosing, based on AUCinf,pred)","Vss (for multiple-dose, based on Clast,obs)","Vss (for multiple-dose, based on Clast,pred)","Vd (based on Clast,obs)","Vd (based on Clast,pred)","Effective half-life (based on MRT,obs)","Effective half-life (based on MRT,pred)","Effective half-life (for IV dosing, based on MRT,obs)","Effective half-life (for IV dosing, based on MRT,pred)","Kel (based on AUCinf,obs)","Kel (based on AUCinf,pred)","Kel (for IV dosing, based on AUCinf,obs)","Kel (for IV dosing, based on AUCinf,pred)","AUClast (dose-normalized)","AUCall (dose-normalized)","AUCinf,obs (dose-normalized)","AUCinf,pred (dose-normalized)","AUMC,last (dose-normalized)","AUMC,all (dose-normalized)","AUMC,inf,obs (dose-normalized)","AUMC,inf,pred (dose-normalized)","Cmax (dose-normalized)","Cmin (dose-normalized)","Clast (dose-normalized)","Clast,pred (dose-normalized)","Cav (dose-normalized)","Ctrough (dose-normalized)"],["Starting time of the interval","Ending time of the interval (potentially infinity)","The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification","The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification","The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification","The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification plus the moment of the triangle from that last concentration to 0 at the first concentration below the limit of quantification","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations","Initial concentration after an IV bolus","Maximum observed concentration","Minimum observed concentration","Time of the maximum observed concentration","Time of the last concentration observed above the limit of quantification","Time of the first concentration above the limit of quantification","The last concentration observed above the limit of quantification","Clearance or observed oral clearance calculated to Clast","Clearance or observed oral clearance calculated with AUCall","Bioavailability or relative bioavailability","The mean residence time to the last observed concentration above the LOQ","The mean residence time to the last observed concentration above the LOQ correcting for dosing duration","The steady-state volume of distribution calculating through Tlast","The steady-state volume of distribution with intravenous infusion calculating through Tlast","The average concentration during an interval","The trough (end of interval) concentration","The predose concentration","Peak-to-Trough ratio (fraction)","Lag time","Degree of fluctuation","Swing relative to Cmin","Concentration at the end of infusion","The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero","The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero","The amount excreted (typically into urine or feces)","The renal clearance calculated using AUClast","The renal clearance calculated using AUCinf,obs","The renal clearance calculated using AUCinf,pred","The fraction of the dose excreted","For sparse PK sampling, the area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification","Time above a given concentration","The AUClast calculated with back-extrapolation for intravenous dosing using extrapolated C0","The AUCall calculated with back-extrapolation for intravenous dosing using extrapolated C0","The AUCint,last calculated with back-extrapolation for intravenous dosing using extrapolated C0","The AUCint,all calculated with back-extrapolation for intravenous dosing using extrapolated C0","The back-extrapolation percent for intravenous dosing based on AUClast","The back-extrapolation percent for intravenous dosing based on AUCall","The back-extrapolation percent for intravenous dosing based on AUCint,last","The back-extrapolation percent for intravenous dosing based on AUCint,all","The (terminal) half-life","The r^2 value of the half-life calculation","The adjusted r^2 value of the half-life calculation","The elimination rate of the terminal half-life","The first time point used for the calculation of half-life","The number of points used for the calculation of half-life","The concentration at Tlast as predicted by the half-life","The ratio of the half-life to the duration used for half-life calculation","The effective half-life (as determined from the MRTlast)","The effective half-life (as determined from the intravenous MRTlast)","Elimination rate (as calculated from the MRT using AUClast)","Elimination rate (as calculated from the intravenous MRTlast)","The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast","The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast","The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast","The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations","The AUCinf,obs calculated with back-extrapolation for intravenous dosing using extrapolated C0","The  calculated with back-extrapolation for intravenous dosing using extrapolated C0","The back-extrapolation percent for intravenous dosing based on AUCinf,obs","The back-extrapolation percent for intravenous dosing based on AUCinf,pred","Percent of the AUCinf that is extrapolated after Tlast calculated from the observed Clast","Percent of the AUCinf that is extrapolated after Tlast calculated from the predicted Clast","Clearance or observed oral clearance calculated with observed Clast","Clearance or observed oral clearance calculated with predicted Clast","The mean residence time to infinity using observed Clast","The mean residence time to infinity using predicted Clast","The mean residence time to infinity using observed Clast correcting for dosing duration","The mean residence time to infinity using predicted Clast correcting for dosing duration","The mean residence time with multiple dosing and nonlinear kinetics using observed Clast","The mean residence time with multiple dosing and nonlinear kinetics using predicted Clast","The terminal volume of distribution using observed Clast","The terminal volume of distribution using predicted Clast","The steady-state volume of distribution using observed Clast","The steady-state volume of distribution using predicted Clast","The steady-state volume of distribution with intravenous infusion using observed Clast","The steady-state volume of distribution with intravenous infusion using predicted Clast","The steady-state volume of distribution for nonlinear multiple-dose data using observed Clast","The steady-state volume of distribution for nonlinear multiple-dose data using predicted Clast","Apparent observed volume of distribution calculated with observed Clast","Apparent observed volume of distribution calculated with predicted Clast","The effective half-life (as determined from the MRTobs)","The effective half-life (as determined from the MRTpred)","The effective half-life (as determined from the intravenous MRTobs)","The effective half-life (as determined from the intravenous 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Start","Interval End","AUClast","AUCall","AUMC,last","AUMC,all","AUCint (based on AUClast extrapolation)","AUCint (based on AUClast extrapolation, dose-aware)","AUCint (based on AUCall extrapolation)","AUCint (based on AUCall extrapolation, dose-aware)","C0","Cmax","Cmin","Tmax","Tlast","Tfirst","Clast","CL (based on AUClast)","CL (based on AUCall)","Bioavailability","MRT (based on AUClast)","MRT (for IV dosing, based on AUClast)","Vss (based on AUClast)","Vss (for IV dosing, based on AUClast)","Cav","Ctrough","Cstart","Peak-to-trough ratio","Tlag","Degree of fluctuation","Swing","Ceoi","AUC,above","AUC,above","Amount excreted","Renal clearance (from AUClast)","Renal clearance (from AUCinf,obs)","Renal clearance (from AUCinf,pred)","Fraction excreted","Sparse AUClast","Time above Concentration","AUClast (IV dosing)","AUCall (IV dosing)","AUCint,last (IV dosing)","AUCint,all (IV dosing)","AUCbext (based on AUClast)","AUCbext (based on AUCall)","AUCbext (based on AUCint,last)","AUCbext (based on AUCint,all)","Half-life","$r^2$","$r^2_{adj}$","$\\lambda_z$","First time for $\\lambda_z$","Number of points used for lambda_z","Clast,pred","Span ratio","Effective half-life (based on MRT,last)","Effective half-life (for IV dosing, based on MRTlast)","Kel (based on AUClast)","Kel (for IV dosing, based on AUClast)","AUCinf,obs","AUCinf,pred","AUMC,inf,obs","AUMC,inf,pred","AUCint (based on AUCinf,obs extrapolation)","AUCint (based on AUCinf,obs extrapolation, dose-aware)","AUCint (based on AUCinf,pred extrapolation)","AUCint (based on AUCinf,pred extrapolation, dose-aware)","AUCinf,obs (IV dosing)","AUCinf,pred (IV dosing)","AUCbext (based on AUCinf,obs)","AUCbext (based on AUCinf,pred)","AUCpext (based on AUCinf,obs)","AUCpext (based on AUCinf,pred)","CL (based on AUCinf,obs)","CL (based on AUCinf,pred)","MRT (based on AUCinf,obs)","MRT (based on AUCinf,pred)","MRT (for IV dosing, based on AUCinf,obs)","MRT (for IV dosing, based on AUCinf,pred)","MRT (for multiple dosing, 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(dose-normalized)","Cmax (dose-normalized)","Cmin (dose-normalized)","Clast (dose-normalized)","Clast,pred (dose-normalized)","Cav (dose-normalized)","Ctrough (dose-normalized)"],["Starting time of the interval","Ending time of the interval (potentially infinity)","The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification","The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification","The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification","The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification plus the moment of the triangle from that last concentration to 0 at the first concentration below the limit of quantification","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations","Initial concentration after an IV bolus","Maximum observed concentration","Minimum observed concentration","Time of the maximum observed concentration","Time of 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time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero","The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero","The amount excreted (typically into urine or feces)","The renal clearance calculated using AUClast","The renal clearance calculated using AUCinf,obs","The renal clearance calculated using AUCinf,pred","The fraction of the dose excreted","For sparse PK sampling, the area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification","Time above a given concentration","The AUClast calculated with back-extrapolation for intravenous dosing using extrapolated C0","The AUCall calculated with back-extrapolation for intravenous dosing using extrapolated C0","The AUCint,last calculated with back-extrapolation for intravenous dosing using extrapolated C0","The AUCint,all calculated with back-extrapolation for intravenous dosing using extrapolated C0","The back-extrapolation percent for intravenous dosing based on AUClast","The back-extrapolation percent for intravenous dosing based on AUCall","The back-extrapolation percent for intravenous dosing based on AUCint,last","The back-extrapolation percent for intravenous dosing based on AUCint,all","The (terminal) half-life","The r^2 value of the half-life calculation","The adjusted r^2 value of the half-life calculation","The elimination rate of the terminal half-life","The first time point used for the calculation of half-life","The number of points used for the calculation of half-life","The concentration at Tlast as predicted by the half-life","The ratio of the half-life to the duration used for half-life calculation","The effective half-life (as determined from the MRTlast)","The effective half-life (as determined from the intravenous MRTlast)","Elimination rate (as calculated from the MRT using AUClast)","Elimination rate (as calculated from the intravenous MRTlast)","The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast","The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast","The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast","The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall)","The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations","The AUCinf,obs calculated with back-extrapolation for intravenous dosing using extrapolated C0","The  calculated with back-extrapolation for intravenous dosing using extrapolated C0","The back-extrapolation percent for intravenous dosing based on AUCinf,obs","The back-extrapolation percent for intravenous dosing based on AUCinf,pred","Percent of the AUCinf that is extrapolated after Tlast calculated from the observed Clast","Percent of the AUCinf that is extrapolated after Tlast calculated from the predicted Clast","Clearance or observed oral clearance calculated with observed Clast","Clearance or observed oral clearance calculated with predicted Clast","The mean residence time to infinity using observed Clast","The mean residence time to infinity using predicted Clast","The mean residence time to infinity using observed Clast correcting for dosing duration","The mean residence time to infinity using predicted Clast correcting for dosing duration","The mean residence time with multiple dosing and nonlinear kinetics using observed Clast","The mean residence time with multiple dosing and nonlinear kinetics using predicted Clast","The terminal volume of distribution using observed Clast","The terminal volume of distribution using predicted Clast","The steady-state volume of distribution using observed Clast","The steady-state volume of distribution using predicted Clast","The steady-state volume of distribution with intravenous infusion using observed Clast","The steady-state volume of distribution with intravenous infusion using predicted Clast","The steady-state volume of distribution for nonlinear multiple-dose data using observed Clast","The steady-state volume of distribution for nonlinear multiple-dose data using predicted Clast","Apparent observed volume of distribution calculated with observed Clast","Apparent observed volume of distribution calculated with predicted Clast","The effective half-life (as determined from the MRTobs)","The effective half-life (as determined from the MRTpred)","The effective half-life (as determined from the intravenous MRTobs)","The effective half-life (as determined from the intravenous 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@@ -1551,7 +1552,7 @@ <h2 id="configuration-file">Configuration File<a class="anchor" aria-label="anch
 
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   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/articles/noncompartmental_analysis_files/htmltools-fill-0.5.8/fill.css b/docs/articles/noncompartmental_analysis_files/htmltools-fill-0.5.8/fill.css
new file mode 100644
index 0000000..841ea9d
--- /dev/null
+++ b/docs/articles/noncompartmental_analysis_files/htmltools-fill-0.5.8/fill.css
@@ -0,0 +1,21 @@
+@layer htmltools {
+  .html-fill-container {
+    display: flex;
+    flex-direction: column;
+    /* Prevent the container from expanding vertically or horizontally beyond its
+    parent's constraints. */
+    min-height: 0;
+    min-width: 0;
+  }
+  .html-fill-container > .html-fill-item {
+    /* Fill items can grow and shrink freely within
+    available vertical space in fillable container */
+    flex: 1 1 auto;
+    min-height: 0;
+    min-width: 0;
+  }
+  .html-fill-container > :not(.html-fill-item) {
+    /* Prevent shrinking or growing of non-fill items */
+    flex: 0 0 auto;
+  }
+}
diff --git a/docs/articles/rmdhunks/simulate_rules.html b/docs/articles/rmdhunks/simulate_rules.html
index 2566e92..37fb26d 100644
--- a/docs/articles/rmdhunks/simulate_rules.html
+++ b/docs/articles/rmdhunks/simulate_rules.html
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+<!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="">
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@@ -321,7 +321,7 @@ <h4 id="the-following-functions-will-be-available">The following functions will
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/authors.html b/docs/authors.html
index c7a3a05..d910736 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -106,7 +106,7 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
index ac54b39..7530c13 100644
--- a/docs/deps/bootstrap-5.3.1/bootstrap.min.css
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -2,4 +2,4 @@
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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(244,234,202,0.7);--bs-navbar-hover-color: #F4EACA;--bs-navbar-disabled-color: rgba(244,234,202,0.25);--bs-navbar-active-color: #f4eaca;--bs-navbar-brand-color: #f4eaca;--bs-navbar-brand-hover-color: #f4eaca;--bs-navbar-toggler-border-color: rgba(244,234,202,0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28244,234,202,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28244,234,202,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: 0;--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-.5 * var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid 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var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media (min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23185566'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2349291c'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #dbb3a3;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(183,103,71,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23d4a491'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23d4a491'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 3px;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #628687;--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(183,103,71,0.25);--bs-pagination-active-color: #F4EACA;--bs-pagination-active-bg: #B76747;--bs-pagination-active-border-color: #a55d40;--bs-pagination-disabled-color: #92A89E;--bs-pagination-disabled-bg: rgba(0,0,0,0);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: 1rem;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 300;--bs-badge-color: #F4EACA;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:400;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #F4EACA;--bs-progress-bar-bg: #B76747;--bs-progress-bar-transition: width 0.6s 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var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #F4EACA;--bs-list-group-active-bg: #B76747;--bs-list-group-active-border-color: #B76747;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: 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var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #628687;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23628687'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .6;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(183,103,71,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * 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!important}.link-light{color:RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-light:hover,.link-light:focus{color:RGBA(207,212,195, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(207,212,195, var(--bs-link-underline-opacity, 1)) !important}.link-dark{color:RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-dark:hover,.link-dark:focus{color:RGBA(39,81,90, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(39,81,90, var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis:hover,.link-body-emphasis:focus{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 0.75)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 0.75)) !important}.focus-ring:focus{outline:0;box-shadow:var(--bs-focus-ring-x, 0) var(--bs-focus-ring-y, 0) var(--bs-focus-ring-blur, 0) var(--bs-focus-ring-width) var(--bs-focus-ring-color)}.icon-link{display:inline-flex;gap:.375rem;align-items:center;-webkit-align-items:center;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 0.5));text-underline-offset:.25em;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden}.icon-link>.bi{flex-shrink:0;-webkit-flex-shrink:0;width:1em;height:1em;fill:currentcolor;transition:0.2s ease-in-out transform}@media (prefers-reduced-motion: 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.25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28244,234,202,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(244,234,202,0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(244,234,202,0.7);--bs-navbar-hover-color: #F4EACA;--bs-navbar-disabled-color: rgba(244,234,202,0.25);--bs-navbar-active-color: #f4eaca;--bs-navbar-brand-color: #f4eaca;--bs-navbar-brand-hover-color: #f4eaca;--bs-navbar-toggler-border-color: rgba(244,234,202,0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28244,234,202,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28244,234,202,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: 0;--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: 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var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media (min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - 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#dbb3a3;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(183,103,71,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23d4a491'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23d4a491'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 3px;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #628687;--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(183,103,71,0.25);--bs-pagination-active-color: #F4EACA;--bs-pagination-active-bg: #B76747;--bs-pagination-active-border-color: #a55d40;--bs-pagination-disabled-color: #92A89E;--bs-pagination-disabled-bg: rgba(0,0,0,0);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: 1rem;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 300;--bs-badge-color: #F4EACA;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:400;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #F4EACA;--bs-progress-bar-bg: #B76747;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(244,234,202,0.15) 25%, transparent 25%, transparent 50%, rgba(244,234,202,0.15) 50%, rgba(244,234,202,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #F4EACA;--bs-list-group-active-bg: #B76747;--bs-list-group-active-border-color: #B76747;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #628687;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,68,91,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: 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diff --git a/docs/deps/bootstrap-5.3.1/font.css b/docs/deps/bootstrap-5.3.1/font.css
index ed5c7cf..2bf0881 100644
--- a/docs/deps/bootstrap-5.3.1/font.css
+++ b/docs/deps/bootstrap-5.3.1/font.css
@@ -1,48 +1,600 @@
+/* cyrillic-ext */
 @font-face {
   font-family: 'Open Sans';
   font-style: italic;
   font-weight: 300;
-  font-stretch: normal;
+  font-stretch: 100%;
   font-display: swap;
-  src: url(fonts/memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff) format('woff');
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
 }
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
 @font-face {
   font-family: 'Open Sans';
   font-style: italic;
   font-weight: 400;
-  font-stretch: normal;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
   font-display: swap;
-  src: url(fonts/memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkaVQ.woff) format('woff');
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
+/* latin */
 @font-face {
   font-family: 'Open Sans';
   font-style: italic;
   font-weight: 700;
-  font-stretch: normal;
+  font-stretch: 100%;
   font-display: swap;
-  src: url(fonts/memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjaVQ.woff) format('woff');
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
+/* cyrillic-ext */
 @font-face {
   font-family: 'Open Sans';
   font-style: normal;
   font-weight: 300;
-  font-stretch: normal;
+  font-stretch: 100%;
   font-display: swap;
-  src: url(fonts/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0C4k.woff) format('woff');
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
 }
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 300;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
 @font-face {
   font-family: 'Open Sans';
   font-style: normal;
   font-weight: 400;
-  font-stretch: normal;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
   font-display: swap;
-  src: url(fonts/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0C4k.woff) format('woff');
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
+/* latin */
 @font-face {
   font-family: 'Open Sans';
   font-style: normal;
   font-weight: 700;
-  font-stretch: normal;
+  font-stretch: 100%;
   font-display: swap;
-  src: url(fonts/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1y4k.woff) format('woff');
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
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 <meta property="og:description" content="Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of ruminate is to create a shiny interface to make these tools more broadly available while creating reproducible results.">
 <meta property="og:image" content="/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -246,7 +246,7 @@ <h2 data-toc-skip>Dev status</h2>
 
 <div class="pkgdown-footer-right">
   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/news/index.html b/docs/news/index.html
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 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -109,7 +109,7 @@ <h2 class="pkg-version" data-toc-text="0.1.1" id="ruminate-011">ruminate 0.1.1<a
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 0df0a8a..fabde71 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,5 +1,5 @@
 pandoc: 3.1.1
-pkgdown: 2.0.7
+pkgdown: 2.0.6
 pkgdown_sha: ~
 articles:
   clinical_trial_simulation: clinical_trial_simulation.html
@@ -7,5 +7,5 @@ articles:
   model_builder: model_builder.html
   noncompartmental_analysis: noncompartmental_analysis.html
   simulate_rules: rmdhunks/simulate_rules.html
-last_built: 2024-04-01T01:06Z
+last_built: 2024-04-16T14:25Z
 
diff --git a/docs/reference/CTS_Server.html b/docs/reference/CTS_Server.html
index 8e76154..49edc25 100644
--- a/docs/reference/CTS_Server.html
+++ b/docs/reference/CTS_Server.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Server function for the Clinical Trial Simulator  Shiny Module"><title>Clinical Trial Simulator State Server — CTS_Server • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Clinical Trial Simulator State Server — CTS_Server"><meta property="og:description" content="Server function for the Clinical Trial Simulator  Shiny Module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Server function for the Clinical Trial Simulator  Shiny Module"><title>Clinical Trial Simulator State Server — CTS_Server • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Clinical Trial Simulator State Server — CTS_Server"><meta property="og:description" content="Server function for the Clinical Trial Simulator  Shiny Module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -137,7 +137,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_add_covariate.html b/docs/reference/CTS_add_covariate.html
index cb1522c..6fa3a9b 100644
--- a/docs/reference/CTS_add_covariate.html
+++ b/docs/reference/CTS_add_covariate.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the ui elements in the module state and processes the covariate elements for addition."><title>Add Covariate to Elemetnt — CTS_add_covariate • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Add Covariate to Elemetnt — CTS_add_covariate"><meta property="og:description" content="Takes the ui elements in the module state and processes the covariate elements for addition."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the ui elements in the module state and processes the covariate elements for addition."><title>Add Covariate to Elemetnt — CTS_add_covariate • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Add Covariate to Elemetnt — CTS_add_covariate"><meta property="og:description" content="Takes the ui elements in the module state and processes the covariate elements for addition."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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 <![endif]--></head><body>
@@ -104,9 +104,9 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>This depends on the following UI values in the state</p><ul><li><p>state["CTS"]["ui"]["covariate_value"]</p></li>
-<li><p>state["CTS"]["ui"]["covariate_type_selected"]</p></li>
-<li><p>state["CTS"]["ui"]["selected_covariate"]</p></li>
+    <p>This depends on the following UI values in the state</p><ul><li><p><code>state[["CTS"]][["ui"]][["covariate_value"]]</code></p></li>
+<li><p><code>state[["CTS"]][["ui"]][["covariate_type_selected"]]</code></p></li>
+<li><p><code>state[["CTS"]][["ui"]][["selected_covariate"]]</code></p></li>
 </ul></div>
 
     <div class="section level2">
@@ -115,7 +115,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>package ‘formods’ was built under R version 4.2.3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading formods</span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -150,7 +153,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: a6517a625b3252139391dc2e9f59f50a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:3149cb1e017d6388b2898f3c772486b4</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: a890472739a3bfdcf1611d936eeba15b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 53e1afc18174e7a020ff9f75bb34c963</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: fa03ed2bad3415184a53a5acfff96b8a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15c571c86d03c87893193a8e68035492</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -162,7 +224,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_add_rule.html b/docs/reference/CTS_add_rule.html
index bc648ef..98be354 100644
--- a/docs/reference/CTS_add_rule.html
+++ b/docs/reference/CTS_add_rule.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the ui elements in the state and element and attempts to add a rule."><title>Add Rule to Element — CTS_add_rule • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Add Rule to Element — CTS_add_rule"><meta property="og:description" content="Takes the ui elements in the state and element and attempts to add a rule."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -131,7 +131,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -166,7 +167,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 08583be5e6d971169e6ab1366416c505</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: e79d2623868730282770f9284cdf03dd</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: cd4c562a774ff3fbe46a0c34d478c05e</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 44875efaa01517e967afab541affccc9</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -178,7 +238,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_append_report.html b/docs/reference/CTS_append_report.html
index 428e0a9..acdf881 100644
--- a/docs/reference/CTS_append_report.html
+++ b/docs/reference/CTS_append_report.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Appends report elements to a formods report."><title>Append Report Elements — CTS_append_report • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Report Elements — CTS_append_report"><meta property="og:description" content="Appends report elements to a formods report."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Appends report elements to a formods report."><title>Append Report Elements — CTS_append_report • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Report Elements — CTS_append_report"><meta property="og:description" content="Appends report elements to a formods report."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -128,7 +128,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_change_source_model.html b/docs/reference/CTS_change_source_model.html
index 15d0d09..3585431 100644
--- a/docs/reference/CTS_change_source_model.html
+++ b/docs/reference/CTS_change_source_model.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the ui elements in the state and element and processes any changes to the source model and updates the element accordingly."><title>Change the Source Model — CTS_change_source_model • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Change the Source Model — CTS_change_source_model"><meta property="og:description" content="Takes the ui elements in the state and element and processes any changes to the source model and updates the element accordingly."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the ui elements in the state and element and processes any changes to the source model and updates the element accordingly."><title>Change the Source Model — CTS_change_source_model • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Change the Source Model — CTS_change_source_model"><meta property="og:description" content="Takes the ui elements in the state and element and processes any changes to the source model and updates the element accordingly."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -110,7 +110,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -145,7 +146,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 935881096cbd16e6975d650488038bbf</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: f44ed9bf2c959f9a04068b814e4f068f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c80ec5c0395e9ecc4faa148bb59760ea</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 6f8380ea8efd3e6983d5f68af8741c26</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -157,7 +217,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_del_current_element.html b/docs/reference/CTS_del_current_element.html
index dda2254..62d4edd 100644
--- a/docs/reference/CTS_del_current_element.html
+++ b/docs/reference/CTS_del_current_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a CTS state and deletes the current cohort.
-If that is the last element, then a new default will be added."><title>Deletes Current cohort — CTS_del_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Deletes Current cohort — CTS_del_current_element"><meta property="og:description" content="Takes a CTS state and deletes the current cohort.
+If that is the last element, then a new default will be added."><title>Deletes Current cohort — CTS_del_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Deletes Current cohort — CTS_del_current_element"><meta property="og:description" content="Takes a CTS state and deletes the current cohort.
 If that is the last element, then a new default will be added."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -105,7 +105,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -140,7 +141,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 9322acda5a3f497a6a34a643af97572d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: cc2d97ebe541372ee34e9c1156331c50</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 1f97f308e2c855147b2c07f565f83970</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: e4a7db4c31e85accf7c88c16eaf9bb86</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -152,7 +212,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_fetch_code.html b/docs/reference/CTS_fetch_code.html
index 332bd1c..c9da21b 100644
--- a/docs/reference/CTS_fetch_code.html
+++ b/docs/reference/CTS_fetch_code.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the code to generate results seen in the app"><title>Fetch Module Code — CTS_fetch_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Code — CTS_fetch_code"><meta property="og:description" content="Fetches the code to generate results seen in the app"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the code to generate results seen in the app"><title>Fetch Module Code — CTS_fetch_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Code — CTS_fetch_code"><meta property="og:description" content="Fetches the code to generate results seen in the app"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -102,7 +102,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -137,7 +138,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 59c707ab86c6228f7ff33481cd21655a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 88ad6e89f4b6392b2ef8eacdd2598f0b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 0a5804e808d8bce5186653bfe1d2bced</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 8d0060c6f0d82de4c3d99ec0b44d99e7</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -149,7 +209,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_fetch_current_element.html b/docs/reference/CTS_fetch_current_element.html
index d2d3777..402a268 100644
--- a/docs/reference/CTS_fetch_current_element.html
+++ b/docs/reference/CTS_fetch_current_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a CTS state and returns the current active
-cohort"><title>Fetches Current cohort — CTS_fetch_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Current cohort — CTS_fetch_current_element"><meta property="og:description" content="Takes a CTS state and returns the current active
+cohort"><title>Fetches Current cohort — CTS_fetch_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Current cohort — CTS_fetch_current_element"><meta property="og:description" content="Takes a CTS state and returns the current active
 cohort"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -107,7 +107,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -142,7 +143,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 76a18c3abc6e9f1f4708db64e30e2e31</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 9f33b1700fde5dd80ea4badc2569a61c</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 531532f571eebea1826ccc63c17fb98a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: b68fababd66ddda98db4403f7e8b1c70</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -154,7 +214,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_fetch_ds.html b/docs/reference/CTS_fetch_ds.html
index 3f2ce29..07ba0ec 100644
--- a/docs/reference/CTS_fetch_ds.html
+++ b/docs/reference/CTS_fetch_ds.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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@@ -136,14 +136,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 26cc561933399401beb815326445fdcd</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:3a7eaf3460a9f8ed4f6962eec3be80c2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: c499d1b8aa1202cd5d49e9985c47e057</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:ea7a2403d31e0510338cd097a537d09d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
@@ -155,12 +155,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -169,8 +169,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 16bbb3aeea9892cf6a53a06e3666ff5c</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 0972fd72a2d40171e7df04cff4ce9d34</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: f5a0a431e4e9e627930edebefd192455</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: aeab9fffaba1ec1cd917ec30144b0e90</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: source model change detected</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:  &gt; covariates reset</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
@@ -181,8 +181,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 384629ad91b10777e86333780047de11</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 8f3832337cda6fcfb49bcac8c62d91ae</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 1b4ac211aa2c5b86e6c80fe5ee0086aa</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 206b2fb29f12642234b1952d9cd188f2</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -215,8 +215,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 128f7e0a040a7eb62ee2e7ef0b8557b9</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 0c3803f00431fb44fbc1020644b26ef2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c2deb5a6d2303193adec2ddfa561f53d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 165af86bb00da1f8cf3e6a84ec76d792</span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">ds</span> <span class="op">=</span> <span class="fu">CTS_fetch_ds</span><span class="op">(</span><span class="va">state</span><span class="op">)</span></span></span>
@@ -245,7 +245,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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     </footer></div>
diff --git a/docs/reference/CTS_fetch_sc_meta.html b/docs/reference/CTS_fetch_sc_meta.html
index afcb77f..cf81d21 100644
--- a/docs/reference/CTS_fetch_sc_meta.html
+++ b/docs/reference/CTS_fetch_sc_meta.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This provides meta information about simulatino options. This
-includes option names, text descriptions, ui_names used, etc."><title>Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta"><meta property="og:description" content="This provides meta information about simulatino options. This
+includes option names, text descriptions, ui_names used, etc."><title>Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta"><meta property="og:description" content="This provides meta information about simulatino options. This
 includes option names, text descriptions, ui_names used, etc."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -324,7 +324,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_fetch_state.html b/docs/reference/CTS_fetch_state.html
index 8afbbe5..eb60fc7 100644
--- a/docs/reference/CTS_fetch_state.html
+++ b/docs/reference/CTS_fetch_state.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch Clinical Trial Simulator State — CTS_fetch_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Clinical Trial Simulator State — CTS_fetch_state"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch Clinical Trial Simulator State — CTS_fetch_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Clinical Trial Simulator State — CTS_fetch_state"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -170,14 +170,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
@@ -189,12 +189,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -203,8 +203,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 224113a90fd21bfb2af49de3214fb2a2</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 437596b28f915895d6a884ba41e1c3e1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 360f8b9307c486e662836148e1f02e0c</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 194c5a9d38b07c3f460584172c991b84</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: source model change detected</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:  &gt; covariates reset</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
@@ -215,8 +215,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 384629ad91b10777e86333780047de11</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 475d2d0e93115c5a1924b97fcfd04e6a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 1b4ac211aa2c5b86e6c80fe5ee0086aa</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: c243ca52aba9ad8a3bb2228dedcd0b67</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -249,8 +249,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 6d48d1dd9570433063d16767b8aab7cb</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: f5cfc55ad123ff198550822db26c76d6</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: f02d4b20743252d7d21abfa8412c0dc5</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: f51e52188412a77c702aaee3432b40ef</span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
 <span class="r-in"><span><span class="va">input</span>   <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">input</span></span></span>
 <span class="r-in"><span></span></span>
@@ -278,7 +278,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_init_state.html b/docs/reference/CTS_init_state.html
index 8637990..f279185 100644
--- a/docs/reference/CTS_init_state.html
+++ b/docs/reference/CTS_init_state.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a list of the initialized module state"><title>Initialize CTS Module State — CTS_init_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize CTS Module State — CTS_init_state"><meta property="og:description" content="Creates a list of the initialized module state"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a list of the initialized module state"><title>Initialize CTS Module State — CTS_init_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize CTS Module State — CTS_init_state"><meta property="og:description" content="Creates a list of the initialized module state"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -138,14 +138,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
@@ -157,12 +157,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -171,8 +171,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: e9f6c8e3b3aac0f67b7ba01990565c45</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: b91782c28c672958b500b0aa26f62ebe</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: cc14b137e9e94bb79d7bc711f5bc8517</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 76cc0ad8bbc4ebb0a4ded24b0a3afd09</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: source model change detected</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:  &gt; covariates reset</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
@@ -183,8 +183,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 384629ad91b10777e86333780047de11</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: eb776cbacab36d2d2bb2d13c6c66d750</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 1b4ac211aa2c5b86e6c80fe5ee0086aa</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 1abdae1e84ac314befa02951b742bee4</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -217,8 +217,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: a0ea53dbc455f2b03311669c0c19a5ea</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: b07c64e0db4d0316e05e134a6369112f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 37a6484c228e41e5096bd6a6ad9b8994</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 166cd3d4d74267506ed1235972ff038b</span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
 <span class="r-in"><span><span class="va">input</span>   <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">input</span></span></span>
 <span class="r-in"><span></span></span>
@@ -569,7 +569,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$button_clk_runsim$tooltip</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Run the current simulation."</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Simulate or resimulate the cohort with the current rules applied."</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$button_clk_runsim$tooltip_position</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "right"</span>
@@ -636,7 +636,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "sm"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$button_clk_add_rule$block</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$button_clk_add_rule$tooltip</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Add a new rule."</span>
@@ -681,12 +681,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_state</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_state$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_values</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_values$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_values$placeholder</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "c(1, 1, 1)"</span>
@@ -697,7 +697,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_times</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_times$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_times$placeholder</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "c(0, 7, 14)"</span>
@@ -711,7 +711,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_durations</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_durations$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_dosing_durations$placeholder</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "c(0, 0, 0)"</span>
@@ -725,12 +725,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_set_state_state</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_set_state_state$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_set_state_value</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_set_state_value$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_set_state_value$placeholder</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "A value like 20 or an expression in terms of a system element such as Ac/2"</span>
@@ -741,10 +741,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_manual_code</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_manual_code$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 400</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_manual_code$height</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 75</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$action_manual_code$placeholder</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "SI_interval_ev = etRbind(SI_interval_ev,\n  et(cmt  = 'Ac',\n     id   = id,\n     amt  = 10,\n     evid = 4,\n     time = time))"</span>
@@ -755,7 +755,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_type</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_type$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 100</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 300</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_type$options</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_type$options$dose</span>
@@ -891,7 +891,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$selected_covariate</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$selected_covariate$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$selected_covariate$tooltip</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Pick covariate to define."</span>
@@ -902,13 +902,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_name</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_name$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 300</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_name$placeholder</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "first_dose"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_name$tooltip</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "This is a unique identifier for the current rule. It should start witha letter and can contain letters, numbers and underscores."</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "This is a unique identifier for the current rule. It should start with a letter and can contain letters, numbers and underscores."</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$formatting$rule_name$tooltip_position</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bottom"</span>
@@ -1070,7 +1070,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Save"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$runsim_btn</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Run Simulation"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Simulate Cohort"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$update_plot_btn</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Update Plots"</span>
@@ -1162,6 +1162,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$tab_sim_env</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Simulation Environment"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$tab_chrt_ele_covs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Covariates"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$tab_chrt_ele_rules</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Create Rules"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$tab_chrt_ele_trial</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Trial Options"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $MC$labels$tab_chrt_ele_sim</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Simulation Options"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$errors</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$errors$no_sim_found</span>
@@ -1179,7 +1191,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$tooltips$elements</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Show cohort components"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Show cohort rules"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$tooltips$show_code</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Show cohort code"</span>
@@ -1375,7 +1387,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "right"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$covariate_generation$width</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$covariate_generation$types</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MC$covariate_generation$types$discrete</span>
@@ -1649,8 +1661,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1 One compartment model MB_obj_1_rx       MB MB</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2          PK Biomarker MB_obj_2_rx       MB MB</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                           checksum                      MDLchecksum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 bc8090bd2fe05a30d8e0d5b5ef604e24 ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 bc8090bd2fe05a30d8e0d5b5ef604e24 8fe63af71c1f0c6b582646ec3c4c31b0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 faf5d08a8606e06f198cb8a233bcad72 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 faf5d08a8606e06f198cb8a233bcad72 f3dd1904f0f09fe0ed4d13347a620749</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    code</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    MB_obj_1_fcn = function () \n{\n    description &lt;- "One compartment PK model with linear clearance using differential equations"\n    ini({\n        lka &lt;- 0.45\n        label("Absorption rate (Ka)")\n        lcl &lt;- 1\n        label("Clearance (CL)")\n        lvc &lt;- 3.45\n        label("Central volume of distribution (V)")\n        propSd &lt;- c(0, 0.5)\n        label("Proportional residual error (fraction)")\n        etalcl ~ 0.1\n    })\n    model({\n        ka &lt;- exp(lka)\n        cl &lt;- exp(lcl + etalcl)\n        vc &lt;- exp(lvc)\n        kel &lt;- cl/vc\n        d/dt(depot) &lt;- -ka * depot\n        d/dt(central) &lt;- ka * depot - kel * central\n        Cc &lt;- central/vc\n        Cc ~ prop(propSd)\n    })\n}\nMB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2 MB_obj_2_fcn = function () \n{\n    ini({\n        TV_F1 &lt;- fix(2.22044604925031e-16, 0.744, Inf)\n        TV_ka &lt;- c(2.22044604925031e-16, 0.282, Inf)\n        TV_CL &lt;- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\n        TV_Vc &lt;- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\n        TV_Vp &lt;- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\n        TV_Q &lt;- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\n        TV_MW &lt;- fix(2.22044604925031e-16, 140, Inf)\n        TV_BM_IC &lt;- c(2.22044604925031e-16, 1000, Inf)\n        TV_kdeg_BM &lt;- fix(2.22044604925031e-16, 0.1, Inf)\n        TV_Emax &lt;- c(2.22044604925031e-16, 100, Inf)\n        TV_EC50 &lt;- fix(2.22044604925031e-16, 300, Inf)\n        prop_err_PK &lt;- c(2.22044604925031e-16, 0.1, Inf)\n        add_err_PK &lt;- c(2.22044604925031e-16, 0.1, Inf)\n        add_err_BM &lt;- c(2.22044604925031e-16, 0.1, Inf)\n        ETAka ~ 0.416\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \n            0.0377, 0.0789)\n        ETAQ ~ 0.699\n        ETABM_IC ~ 0.05\n        ETAEmax ~ 0.1\n    })\n    model({\n        F1 = TV_F1\n        ka = TV_ka * exp(ETAka)\n        CL = exp(TV_CL) * exp(ETACL)\n        Vc = exp(TV_Vc) * exp(ETAVc)\n        Cc = Ac/(Vc)\n        Vp = exp(TV_Vp) * exp(ETAVp)\n        Q = exp(TV_Q) * exp(ETAQ)\n        MW = TV_MW\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\n        kdeg_BM = TV_kdeg_BM\n        Emax = TV_Emax * exp(ETAEmax)\n        EC50 = TV_EC50\n        Dinf = 0\n        WTTV = 70\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \n            0.1)\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\n        ksyn_BM = kdeg_BM * BM_IC\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\n        BM(0) = BM_IC\n        d/dt(At) = (-ka * At)\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\n        C_ng_ml = Cc * MW\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\n        BM_obs = BM\n        BM_obs ~ add(add_err_BM)\n    })\n}\nMB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn)</span>
@@ -1658,7 +1670,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$modules</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$modules$MB</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$modules$MB$MB</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bc8090bd2fe05a30d8e0d5b5ef604e24"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "faf5d08a8606e06f198cb8a233bcad72"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -1731,10 +1743,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "MB_obj_1_fcn = function () \n{\n    description &lt;- \"One compartment PK model with linear clearance using differential equations\"\n    ini({\n        lka &lt;- 0.45\n        label(\"Absorption rate (Ka)\")\n        lcl &lt;- 1\n        label(\"Clearance (CL)\")\n        lvc &lt;- 3.45\n        label(\"Central volume of distribution (V)\")\n        propSd &lt;- c(0, 0.5)\n        label(\"Proportional residual error (fraction)\")\n        etalcl ~ 0.1\n    })\n    model({\n        ka &lt;- exp(lka)\n        cl &lt;- exp(lcl + etalcl)\n        vc &lt;- exp(lvc)\n        kel &lt;- cl/vc\n        d/dt(depot) &lt;- -ka * depot\n        d/dt(central) &lt;- ka * depot - kel * central\n        Cc &lt;- central/vc\n        Cc ~ prop(propSd)\n    })\n}\nMB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn)"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$mdl$MB_obj_1_rx$checksum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bc8090bd2fe05a30d8e0d5b5ef604e24"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "faf5d08a8606e06f198cb8a233bcad72"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$mdl$MB_obj_1_rx$MDLchecksum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "ef47c330d3ab2d804b6cec26d53b9683"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "04c4a233276d3bd6cbefe58cce05fbd1"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$mdl$MB_obj_2_rx</span>
@@ -1852,10 +1864,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "MB_obj_2_fcn = function () \n{\n    ini({\n        TV_F1 &lt;- fix(2.22044604925031e-16, 0.744, Inf)\n        TV_ka &lt;- c(2.22044604925031e-16, 0.282, Inf)\n        TV_CL &lt;- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\n        TV_Vc &lt;- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\n        TV_Vp &lt;- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\n        TV_Q &lt;- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\n        TV_MW &lt;- fix(2.22044604925031e-16, 140, Inf)\n        TV_BM_IC &lt;- c(2.22044604925031e-16, 1000, Inf)\n        TV_kdeg_BM &lt;- fix(2.22044604925031e-16, 0.1, Inf)\n        TV_Emax &lt;- c(2.22044604925031e-16, 100, Inf)\n        TV_EC50 &lt;- fix(2.22044604925031e-16, 300, Inf)\n        prop_err_PK &lt;- c(2.22044604925031e-16, 0.1, Inf)\n        add_err_PK &lt;- c(2.22044604925031e-16, 0.1, Inf)\n        add_err_BM &lt;- c(2.22044604925031e-16, 0.1, Inf)\n        ETAka ~ 0.416\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \n            0.0377, 0.0789)\n        ETAQ ~ 0.699\n        ETABM_IC ~ 0.05\n        ETAEmax ~ 0.1\n    })\n    model({\n        F1 = TV_F1\n        ka = TV_ka * exp(ETAka)\n        CL = exp(TV_CL) * exp(ETACL)\n        Vc = exp(TV_Vc) * exp(ETAVc)\n        Cc = Ac/(Vc)\n        Vp = exp(TV_Vp) * exp(ETAVp)\n        Q = exp(TV_Q) * exp(ETAQ)\n        MW = TV_MW\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\n        kdeg_BM = TV_kdeg_BM\n        Emax = TV_Emax * exp(ETAEmax)\n        EC50 = TV_EC50\n        Dinf = 0\n        WTTV = 70\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \n            0.1)\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\n        ksyn_BM = kdeg_BM * BM_IC\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\n        BM(0) = BM_IC\n        d/dt(At) = (-ka * At)\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\n        C_ng_ml = Cc * MW\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\n        BM_obs = BM\n        BM_obs ~ add(add_err_BM)\n    })\n}\nMB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn)"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$mdl$MB_obj_2_rx$checksum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bc8090bd2fe05a30d8e0d5b5ef604e24"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "faf5d08a8606e06f198cb8a233bcad72"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $CTS$MDL$mdl$MB_obj_2_rx$MDLchecksum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "8fe63af71c1f0c6b582646ec3c4c31b0"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "f3dd1904f0f09fe0ed4d13347a620749"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -2289,7 +2301,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/formods/templates/formods.yaml"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MOD_yaml_file</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/RtmpYagqQE/temp_libpathc4691c5677bf/ruminate/templates/CTS.yaml"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/Rtmpke0KwN/temp_libpath790c4fc3c99d/ruminate/templates/CTS.yaml"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $shiny_token</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "non_shiny"</span>
@@ -2305,7 +2317,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_new_element.html b/docs/reference/CTS_new_element.html
index 1bb507f..bfa42bf 100644
--- a/docs/reference/CTS_new_element.html
+++ b/docs/reference/CTS_new_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Appends a new empty cohort to the CTS state object
-and makes this new cohort the active cohort."><title>New ===Module_Name=== cohort — CTS_new_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="New ===Module_Name=== cohort — CTS_new_element"><meta property="og:description" content="Appends a new empty cohort to the CTS state object
+and makes this new cohort the active cohort."><title>New ===Module_Name=== cohort — CTS_new_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="New ===Module_Name=== cohort — CTS_new_element"><meta property="og:description" content="Appends a new empty cohort to the CTS state object
 and makes this new cohort the active cohort."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -107,7 +107,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -142,7 +143,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c8c9f2770a80b6e684999ad3280cf89f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: c5408dd76d32b14bec6868a873035dc1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 5ccbfa31ae9f099494aca239222cee8a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: abb64ec2c533d6039bc04f28cb32c258</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -154,7 +214,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_plot_element.html b/docs/reference/CTS_plot_element.html
index e6c30ec..0855223 100644
--- a/docs/reference/CTS_plot_element.html
+++ b/docs/reference/CTS_plot_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a CTS state and element and simulates the current set of
-rules."><title>Plots the Specified Element — CTS_plot_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plots the Specified Element — CTS_plot_element"><meta property="og:description" content="Takes a CTS state and element and simulates the current set of
+rules."><title>Plots the Specified Element — CTS_plot_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plots the Specified Element — CTS_plot_element"><meta property="og:description" content="Takes a CTS state and element and simulates the current set of
 rules."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -111,7 +111,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -146,7 +147,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 507e79fa161ff67a9609e14b7de357ca</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 7c6f883475f806f068bf644b95c4cda3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: e317aba2dfac4ddfcc44570b6ab050f2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: a2bccd0da1e7904d720bbf938b1b306a</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -158,7 +218,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_set_current_element.html b/docs/reference/CTS_set_current_element.html
index 934a0f3..aed858a 100644
--- a/docs/reference/CTS_set_current_element.html
+++ b/docs/reference/CTS_set_current_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a CTS state and returns the current active
-cohort"><title>Sets the Value for the  Current cohort — CTS_set_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Sets the Value for the  Current cohort — CTS_set_current_element"><meta property="og:description" content="Takes a CTS state and returns the current active
+cohort"><title>Sets the Value for the  Current cohort — CTS_set_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Sets the Value for the  Current cohort — CTS_set_current_element"><meta property="og:description" content="Takes a CTS state and returns the current active
 cohort"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -110,7 +110,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -145,7 +146,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c9ca684414f97a9252ec0fd21d5fc4f2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 19724b2e45135e4868442e2fbdf102d2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 9addee50488e30c656fd49eec9e684a3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 118e8d2e0b8d89848e5fc5814f49df1f</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -157,7 +217,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_simulate_element.html b/docs/reference/CTS_simulate_element.html
index e9cb2e3..86bdb19 100644
--- a/docs/reference/CTS_simulate_element.html
+++ b/docs/reference/CTS_simulate_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a CTS state and element and simulates the current set of
-rules."><title>Simulates the Specified Element — CTS_simulate_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Simulates the Specified Element — CTS_simulate_element"><meta property="og:description" content="Takes a CTS state and element and simulates the current set of
+rules."><title>Simulates the Specified Element — CTS_simulate_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Simulates the Specified Element — CTS_simulate_element"><meta property="og:description" content="Takes a CTS state and element and simulates the current set of
 rules."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -110,7 +110,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -145,7 +146,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: d2a68da2eefbde667d73bdcd09e029f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: f075cf38872f1db04a7f59a90a4e1a8e</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b12f27e10b95d7dcce318b887404d22</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 0e2f7ebea1fdee9388660ad4403d55d1</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -157,7 +217,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_test_mksession.html b/docs/reference/CTS_test_mksession.html
index 1f5a2f3..f2af8a0 100644
--- a/docs/reference/CTS_test_mksession.html
+++ b/docs/reference/CTS_test_mksession.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Populates the supplied session variable for testing."><title>Populate Session Data for Module Testing — CTS_test_mksession • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Populate Session Data for Module Testing — CTS_test_mksession"><meta property="og:description" content="Populates the supplied session variable for testing."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Populates the supplied session variable for testing."><title>Populate Session Data for Module Testing — CTS_test_mksession • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Populate Session Data for Module Testing — CTS_test_mksession"><meta property="og:description" content="Populates the supplied session variable for testing."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -136,8 +136,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
@@ -149,12 +149,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
@@ -163,8 +163,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 85ee0d788dba2ed1c88ef6d5331f13b7</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 5d2ddd21fdfe5ad2b24ddbc7b6bd28a5</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 1f88f4f54c4af6f681203d22c5334e99</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 605a0c0c0399c998fc17097517cce64f</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -176,7 +176,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/CTS_update_checksum.html b/docs/reference/CTS_update_checksum.html
index 46fed50..dcc9747 100644
--- a/docs/reference/CTS_update_checksum.html
+++ b/docs/reference/CTS_update_checksum.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a CTS state and updates the checksum used to trigger
-downstream updates"><title>Updates CTS Module Checksum — CTS_update_checksum • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Updates CTS Module Checksum — CTS_update_checksum"><meta property="og:description" content="Takes a CTS state and updates the checksum used to trigger
+downstream updates"><title>Updates CTS Module Checksum — CTS_update_checksum • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Updates CTS Module Checksum — CTS_update_checksum"><meta property="og:description" content="Takes a CTS state and updates the checksum used to trigger
 downstream updates"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -105,7 +105,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># None of this will work if rxode2 isn't installed:</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu">is_installed</span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://formods.ubiquity.tools/reference/is_installed.html" class="external-link">is_installed</a></span><span class="op">(</span><span class="st">"rxode2"</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">sess_res</span> <span class="op">=</span> <span class="fu"><a href="CTS_test_mksession.html">CTS_test_mksession</a></span><span class="op">(</span>session<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, full_session<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
@@ -140,7 +141,66 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">#'@example inst/test_apps/CTS_funcs.R</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in is_installed("rxode2"):</span> could not find function "is_installed"</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.docx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.pptx")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: including file</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   source: file.path(system.file(package="onbrand"), "templates", "report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS:   dest:   file.path("config","report.yaml")</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: State initialized</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: add rule success</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: rule added</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 1e0c56ce4c763c0b6b6d16301d74ea17</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: b884ffedf741025dae39fc01bb131fc2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>multi-subject simulation without without 'omega'</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: cohort checksum updated: 8685b6b983648242b5574cb4b2725526</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → CTS: module checksum updated: e7385a0ee085d14791c6460ada0457e6</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -152,7 +212,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_Server.html b/docs/reference/MB_Server.html
index c61e533..89b8227 100644
--- a/docs/reference/MB_Server.html
+++ b/docs/reference/MB_Server.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Server function for the Model Builder  Shiny Module"><title>Model Builder State Server — MB_Server • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Model Builder State Server — MB_Server"><meta property="og:description" content="Server function for the Model Builder  Shiny Module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Server function for the Model Builder  Shiny Module"><title>Model Builder State Server — MB_Server • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Model Builder State Server — MB_Server"><meta property="og:description" content="Server function for the Model Builder  Shiny Module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -132,7 +132,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_append_report.html b/docs/reference/MB_append_report.html
index 0c6ec4c..0318007 100644
--- a/docs/reference/MB_append_report.html
+++ b/docs/reference/MB_append_report.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Appends report elements to a formods report."><title>Append Report Elements — MB_append_report • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Report Elements — MB_append_report"><meta property="og:description" content="Appends report elements to a formods report."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Appends report elements to a formods report."><title>Append Report Elements — MB_append_report • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Report Elements — MB_append_report"><meta property="og:description" content="Appends report elements to a formods report."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -128,7 +128,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_build_code.html b/docs/reference/MB_build_code.html
index f17ef61..059b84e 100644
--- a/docs/reference/MB_build_code.html
+++ b/docs/reference/MB_build_code.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the function definition from an rxode object, a function
 object name and an rxode object name and creates the code to build those
-objects."><title>Build Code to Generate Model — MB_build_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Build Code to Generate Model — MB_build_code"><meta property="og:description" content="Takes the function definition from an rxode object, a function
+objects."><title>Build Code to Generate Model — MB_build_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Build Code to Generate Model — MB_build_code"><meta property="og:description" content="Takes the function definition from an rxode object, a function
 object name and an rxode object name and creates the code to build those
 objects."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -148,14 +148,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -301,8 +301,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -467,7 +467,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_del_current_element.html b/docs/reference/MB_del_current_element.html
index f0bae55..5677df1 100644
--- a/docs/reference/MB_del_current_element.html
+++ b/docs/reference/MB_del_current_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a MB state and deletes the current model.
-If that is the last element, then a new default will be added."><title>Deletes Current model — MB_del_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Deletes Current model — MB_del_current_element"><meta property="og:description" content="Takes a MB state and deletes the current model.
+If that is the last element, then a new default will be added."><title>Deletes Current model — MB_del_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Deletes Current model — MB_del_current_element"><meta property="og:description" content="Takes a MB state and deletes the current model.
 If that is the last element, then a new default will be added."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -125,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -278,8 +278,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -444,7 +444,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_fetch_catalog.html b/docs/reference/MB_fetch_catalog.html
index 2b207ba..2220368 100644
--- a/docs/reference/MB_fetch_catalog.html
+++ b/docs/reference/MB_fetch_catalog.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a catalog of the models available in the system file."><title>Fetches List of Available Models — MB_fetch_catalog • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches List of Available Models — MB_fetch_catalog"><meta property="og:description" content="Creates a catalog of the models available in the system file."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a catalog of the models available in the system file."><title>Fetches List of Available Models — MB_fetch_catalog • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches List of Available Models — MB_fetch_catalog"><meta property="og:description" content="Creates a catalog of the models available in the system file."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -130,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -283,8 +283,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -449,7 +449,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_fetch_code.html b/docs/reference/MB_fetch_code.html
index 3543046..1d660fa 100644
--- a/docs/reference/MB_fetch_code.html
+++ b/docs/reference/MB_fetch_code.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the code to generate results seen in the app"><title>Fetch Module Code — MB_fetch_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Code — MB_fetch_code"><meta property="og:description" content="Fetches the code to generate results seen in the app"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the code to generate results seen in the app"><title>Fetch Module Code — MB_fetch_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Code — MB_fetch_code"><meta property="og:description" content="Fetches the code to generate results seen in the app"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -122,14 +122,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -275,8 +275,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -441,7 +441,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_fetch_component.html b/docs/reference/MB_fetch_component.html
index 4db1d23..f3362cb 100644
--- a/docs/reference/MB_fetch_component.html
+++ b/docs/reference/MB_fetch_component.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the selected component of the provided model."><title>Fetch Selected Current Model Component — MB_fetch_component • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Selected Current Model Component — MB_fetch_component"><meta property="og:description" content="Fetches the selected component of the provided model."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the selected component of the provided model."><title>Fetch Selected Current Model Component — MB_fetch_component • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Selected Current Model Component — MB_fetch_component"><meta property="og:description" content="Fetches the selected component of the provided model."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -137,14 +137,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -290,8 +290,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu">MB_fetch_component</span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -456,7 +456,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_fetch_current_element.html b/docs/reference/MB_fetch_current_element.html
index 17312a5..6256100 100644
--- a/docs/reference/MB_fetch_current_element.html
+++ b/docs/reference/MB_fetch_current_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a MB state and returns the current active
-model object."><title>Fetches Current model — MB_fetch_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Current model — MB_fetch_current_element"><meta property="og:description" content="Takes a MB state and returns the current active
+model object."><title>Fetches Current model — MB_fetch_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Current model — MB_fetch_current_element"><meta property="og:description" content="Takes a MB state and returns the current active
 model object."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -127,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -280,8 +280,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -446,7 +446,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_fetch_mdl.html b/docs/reference/MB_fetch_mdl.html
index f1f2a01..12275ff 100644
--- a/docs/reference/MB_fetch_mdl.html
+++ b/docs/reference/MB_fetch_mdl.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the models contained in the module."><title>Fetch Model Builder Module Models — MB_fetch_mdl • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Model Builder Module Models — MB_fetch_mdl"><meta property="og:description" content="Fetches the models contained in the module."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the models contained in the module."><title>Fetch Model Builder Module Models — MB_fetch_mdl • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Model Builder Module Models — MB_fetch_mdl"><meta property="og:description" content="Fetches the models contained in the module."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -134,8 +134,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">mdls</span> <span class="op">=</span> <span class="fu">MB_fetch_mdl</span><span class="op">(</span><span class="va">state</span><span class="op">)</span></span></span>
@@ -153,7 +153,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_fetch_state.html b/docs/reference/MB_fetch_state.html
index 30ab161..d4a3ab8 100644
--- a/docs/reference/MB_fetch_state.html
+++ b/docs/reference/MB_fetch_state.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch Model Builder State — MB_fetch_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Model Builder State — MB_fetch_state"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch Model Builder State — MB_fetch_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Model Builder State — MB_fetch_state"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -165,8 +165,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
 <span class="r-in"><span><span class="va">input</span>   <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">input</span></span></span>
 <span class="r-in"><span></span></span>
@@ -193,7 +193,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_init_state.html b/docs/reference/MB_init_state.html
index 2dbca7b..b116015 100644
--- a/docs/reference/MB_init_state.html
+++ b/docs/reference/MB_init_state.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a list of the initialized module state"><title>Initialize MB Module State — MB_init_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize MB Module State — MB_init_state"><meta property="og:description" content="Creates a list of the initialized module state"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a list of the initialized module state"><title>Initialize MB Module State — MB_init_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize MB Module State — MB_init_state"><meta property="og:description" content="Creates a list of the initialized module state"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -134,8 +134,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
 <span class="r-in"><span><span class="va">input</span>   <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">input</span></span></span>
 <span class="r-in"><span></span></span>
@@ -1271,7 +1271,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/formods/templates/formods.yaml"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $MOD_yaml_file</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/RtmpYagqQE/temp_libpathc4691c5677bf/ruminate/templates/MB.yaml"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/Rtmpke0KwN/temp_libpath790c4fc3c99d/ruminate/templates/MB.yaml"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $shiny_token</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "non_shiny"</span>
@@ -1287,7 +1287,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_new_element.html b/docs/reference/MB_new_element.html
index 731cc16..d549341 100644
--- a/docs/reference/MB_new_element.html
+++ b/docs/reference/MB_new_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Appends a new empty model to the MB state object
-and makes this new model the active model."><title>New Model Building Model — MB_new_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="New Model Building Model — MB_new_element"><meta property="og:description" content="Appends a new empty model to the MB state object
+and makes this new model the active model."><title>New Model Building Model — MB_new_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="New Model Building Model — MB_new_element"><meta property="og:description" content="Appends a new empty model to the MB state object
 and makes this new model the active model."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -127,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -280,8 +280,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -446,7 +446,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_set_current_element.html b/docs/reference/MB_set_current_element.html
index a8dedc7..bbc49ec 100644
--- a/docs/reference/MB_set_current_element.html
+++ b/docs/reference/MB_set_current_element.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a MB state and returns the current active
-model"><title>Sets the Value for the  Current model — MB_set_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Sets the Value for the  Current model — MB_set_current_element"><meta property="og:description" content="Takes a MB state and returns the current active
+model"><title>Sets the Value for the  Current model — MB_set_current_element • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Sets the Value for the  Current model — MB_set_current_element"><meta property="og:description" content="Takes a MB state and returns the current active
 model"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -130,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -283,8 +283,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu">MB_set_current_element</span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -449,7 +449,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_test_catalog.html b/docs/reference/MB_test_catalog.html
index e9c9c28..7fcd352 100644
--- a/docs/reference/MB_test_catalog.html
+++ b/docs/reference/MB_test_catalog.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Reads in models in the catalog and attempts to build them."><title>Tests the Model Catalog — MB_test_catalog • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Tests the Model Catalog — MB_test_catalog"><meta property="og:description" content="Reads in models in the catalog and attempts to build them."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Reads in models in the catalog and attempts to build them."><title>Tests the Model Catalog — MB_test_catalog • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Tests the Model Catalog — MB_test_catalog"><meta property="og:description" content="Reads in models in the catalog and attempts to build them."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -132,14 +132,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -285,8 +285,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -451,7 +451,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_test_mksession.html b/docs/reference/MB_test_mksession.html
index addd5b6..e103715 100644
--- a/docs/reference/MB_test_mksession.html
+++ b/docs/reference/MB_test_mksession.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Populates the supplied session variable for testing."><title>Populate Session Data for Module Testing — MB_test_mksession • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Populate Session Data for Module Testing — MB_test_mksession"><meta property="og:description" content="Populates the supplied session variable for testing."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Populates the supplied session variable for testing."><title>Populate Session Data for Module Testing — MB_test_mksession • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Populate Session Data for Module Testing — MB_test_mksession"><meta property="og:description" content="Populates the supplied session variable for testing."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -132,8 +132,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -145,7 +145,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/reference/MB_update_checksum.html b/docs/reference/MB_update_checksum.html
index 3241479..d25798f 100644
--- a/docs/reference/MB_update_checksum.html
+++ b/docs/reference/MB_update_checksum.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes a MB state and updates the checksum used to trigger
-downstream updates"><title>Update MB Module Checksum — MB_update_checksum • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Update MB Module Checksum — MB_update_checksum"><meta property="og:description" content="Takes a MB state and updates the checksum used to trigger
+downstream updates"><title>Update MB Module Checksum — MB_update_checksum • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Update MB Module Checksum — MB_update_checksum"><meta property="og:description" content="Takes a MB state and updates the checksum used to trigger
 downstream updates"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -125,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
 <span class="r-in"><span><span class="va">input</span>   <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">input</span></span></span>
 <span class="r-in"><span></span></span>
@@ -151,7 +151,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/MB_update_model.html b/docs/reference/MB_update_model.html
index 33fcead..f400c09 100644
--- a/docs/reference/MB_update_model.html
+++ b/docs/reference/MB_update_model.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes an rxode2 object and updates the model components of the
-current element."><title>Updates Current Element with rxode2 Model — MB_update_model • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Updates Current Element with rxode2 Model — MB_update_model"><meta property="og:description" content="Takes an rxode2 object and updates the model components of the
+current element."><title>Updates Current Element with rxode2 Model — MB_update_model • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Updates Current Element with rxode2 Model — MB_update_model"><meta property="og:description" content="Takes an rxode2 object and updates the model components of the
 current element."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -146,14 +146,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72</span>
 <span class="r-in"><span><span class="co"># This is just for CRAN</span></span></span>
 <span class="r-in"><span><span class="co">#sess_res = MB_test_mksession(session=list())</span></span></span>
 <span class="r-in"><span><span class="va">session</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span></span></span>
@@ -299,8 +299,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># You can now place element back in the state</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu"><a href="MB_set_current_element.html">MB_set_current_element</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># This will fetch the current component</span></span></span>
 <span class="r-in"><span><span class="va">component</span> <span class="op">=</span> <span class="fu"><a href="MB_fetch_component.html">MB_fetch_component</a></span><span class="op">(</span><span class="va">state</span>, <span class="va">element</span><span class="op">)</span></span></span>
@@ -465,7 +465,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_Server.html b/docs/reference/NCA_Server.html
index 4745a14..a804263 100644
--- a/docs/reference/NCA_Server.html
+++ b/docs/reference/NCA_Server.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch Non-Compartmental Analysis State — NCA_Server • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Non-Compartmental Analysis State — NCA_Server"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch Non-Compartmental Analysis State — NCA_Server • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Non-Compartmental Analysis State — NCA_Server"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -416,7 +416,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_add_int.html b/docs/reference/NCA_add_int.html
index 2987512..05e26b7 100644
--- a/docs/reference/NCA_add_int.html
+++ b/docs/reference/NCA_add_int.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the start time, stop time, and NCA parameters and adds
-them to the intervals table"><title>Adds Analysis Interval to Current Analysis — NCA_add_int • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Adds Analysis Interval to Current Analysis — NCA_add_int"><meta property="og:description" content="Takes the start time, stop time, and NCA parameters and adds
+them to the intervals table"><title>Adds Analysis Interval to Current Analysis — NCA_add_int • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Adds Analysis Interval to Current Analysis — NCA_add_int"><meta property="og:description" content="Takes the start time, stop time, and NCA parameters and adds
 them to the intervals table"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -120,7 +120,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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     </footer></div>
diff --git a/docs/reference/NCA_append_report.html b/docs/reference/NCA_append_report.html
index d77556d..5e8f204 100644
--- a/docs/reference/NCA_append_report.html
+++ b/docs/reference/NCA_append_report.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Takes an NCA state object and appends any reportable elements
 for the specified report type. On NCA analyses that are in a "good" state
-will be reported. Those not in a good state will be ignored.'><title>Append Report Elements — NCA_append_report • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Report Elements — NCA_append_report"><meta property="og:description" content='Takes an NCA state object and appends any reportable elements
+will be reported. Those not in a good state will be ignored.'><title>Append Report Elements — NCA_append_report • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Report Elements — NCA_append_report"><meta property="og:description" content='Takes an NCA state object and appends any reportable elements
 for the specified report type. On NCA analyses that are in a "good" state
 will be reported. Those not in a good state will be ignored.'><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -215,7 +215,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:e0536abc6e0ba1b0e4f728cc154a5574</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:f47a740b0a21e148117be9dbd00ce49c</span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># here we need an empty report object for tabular data</span></span></span>
@@ -259,7 +259,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_PKNCA_meta.html b/docs/reference/NCA_fetch_PKNCA_meta.html
index 93fa0ef..4b8566b 100644
--- a/docs/reference/NCA_fetch_PKNCA_meta.html
+++ b/docs/reference/NCA_fetch_PKNCA_meta.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Compiles Metadata from PKNCA"><title>Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta"><meta property="og:description" content="Compiles Metadata from PKNCA"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body>
@@ -455,7 +455,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/reference/NCA_fetch_ana_ds.html b/docs/reference/NCA_fetch_ana_ds.html
index 1c5ce00..2bc629d 100644
--- a/docs/reference/NCA_fetch_ana_ds.html
+++ b/docs/reference/NCA_fetch_ana_ds.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the dataset used for the specified analysis"><title>Fetch Analysis Dataset — NCA_fetch_ana_ds • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Analysis Dataset — NCA_fetch_ana_ds"><meta property="og:description" content="Fetches the dataset used for the specified analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the dataset used for the specified analysis"><title>Fetch Analysis Dataset — NCA_fetch_ana_ds • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Analysis Dataset — NCA_fetch_ana_ds"><meta property="og:description" content="Fetches the dataset used for the specified analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -202,7 +202,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:8d3ce4e34528cd358d3211b5f3d1fa32</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:acfc12d32b985885f6ab52a541d0649e</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -263,7 +263,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_ana_pknca.html b/docs/reference/NCA_fetch_ana_pknca.html
index 60431c2..2745d42 100644
--- a/docs/reference/NCA_fetch_ana_pknca.html
+++ b/docs/reference/NCA_fetch_ana_pknca.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the PKNCA output for a specified analysis"><title>Fetch PKNCA Results Object — NCA_fetch_ana_pknca • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch PKNCA Results Object — NCA_fetch_ana_pknca"><meta property="og:description" content="Fetches the PKNCA output for a specified analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the PKNCA output for a specified analysis"><title>Fetch PKNCA Results Object — NCA_fetch_ana_pknca • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch PKNCA Results Object — NCA_fetch_ana_pknca"><meta property="og:description" content="Fetches the PKNCA output for a specified analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -202,7 +202,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:9969a022af47a682c761107bf7822b7e</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:3c072c7957f8861e121dbbe392490d5e</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -263,7 +263,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_code.html b/docs/reference/NCA_fetch_code.html
index 0acb222..e1cca0e 100644
--- a/docs/reference/NCA_fetch_code.html
+++ b/docs/reference/NCA_fetch_code.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the code to generate results seen in the app"><title>Fetch Module Code — NCA_fetch_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Code — NCA_fetch_code"><meta property="og:description" content="Fetches the code to generate results seen in the app"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the code to generate results seen in the app"><title>Fetch Module Code — NCA_fetch_code • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Code — NCA_fetch_code"><meta property="og:description" content="Fetches the code to generate results seen in the app"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -198,7 +198,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:12189e9d19ef3a975d19c5f92e25266b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:c46e0a9de29bb71e3b5200e558d94854</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -259,7 +259,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_current_ana.html b/docs/reference/NCA_fetch_current_ana.html
index 58c11ef..a6bb966 100644
--- a/docs/reference/NCA_fetch_current_ana.html
+++ b/docs/reference/NCA_fetch_current_ana.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes an NCA state and returns the current active analysis"><title>Fetches Current Analysis — NCA_fetch_current_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Current Analysis — NCA_fetch_current_ana"><meta property="og:description" content="Takes an NCA state and returns the current active analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -200,7 +200,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:417faffb9dc4318f681da675cb692e45</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:92aa2dff829b383fb2dfe79a3e1bdbe0</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -261,7 +261,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_current_obj-1.png b/docs/reference/NCA_fetch_current_obj-1.png
index e3c88ff..8dbb4b2 100644
Binary files a/docs/reference/NCA_fetch_current_obj-1.png and b/docs/reference/NCA_fetch_current_obj-1.png differ
diff --git a/docs/reference/NCA_fetch_current_obj.html b/docs/reference/NCA_fetch_current_obj.html
index 981f8ea..3b0c0d2 100644
--- a/docs/reference/NCA_fetch_current_obj.html
+++ b/docs/reference/NCA_fetch_current_obj.html
@@ -3,7 +3,7 @@
 currently selected object. For example if you have specified figure, it will
 look at the output figure selected and the figure number of that figure and
 return the ggplot object for that.
-by subject id highlighting of certain NCA aspects (e.g. points used for half-life)"><title>Fetches the Current Analysis Object — NCA_fetch_current_obj • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches the Current Analysis Object — NCA_fetch_current_obj"><meta property="og:description" content="Takes the current state and object type and returns the
+by subject id highlighting of certain NCA aspects (e.g. points used for half-life)"><title>Fetches the Current Analysis Object — NCA_fetch_current_obj • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches the Current Analysis Object — NCA_fetch_current_obj"><meta property="og:description" content="Takes the current state and object type and returns the
 currently selected object. For example if you have specified figure, it will
 look at the output figure selected and the figure number of that figure and
 return the ggplot object for that.
@@ -211,13 +211,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:6b27b5c20b9e22136fca1148a812ea1f</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:5d866f91b324cf47f621c05e3b35df20</span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Current active table:</span></span></span>
 <span class="r-in"><span><span class="va">res</span> <span class="op">=</span> <span class="fu">NCA_fetch_current_obj</span><span class="op">(</span><span class="va">state</span>, <span class="st">"table"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">res</span><span class="op">$</span><span class="va">ft</span></span></span>
-<style></style><div class="tabwid"><style>.cl-3aff2164{}.cl-3afadf3c{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-3afd1d56{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-3afd283c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd283d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2846{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2847{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2848{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2850{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2851{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2852{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285b{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing="true" class="table cl-3aff2164"><thead><tr style="overflow-wrap:break-word;"><th class="cl-3afd283c"><p class="cl-3afd1d56"><span class="cl-3afadf3c">Time</span></p></th><th colspan="3" class="cl-3afd283d"><p class="cl-3afd1d56"><span class="cl-3afadf3c">Concentration (ng/mL) by ID</span></p></th></tr><tr style="overflow-wrap:break-word;"><th class="cl-3afd2847"><p class="cl-3afd1d56"><span class="cl-3afadf3c">(day)</span></p></th><th class="cl-3afd2848"><p class="cl-3afd1d56"><span class="cl-3afadf3c">1</span></p></th><th class="cl-3afd2848"><p class="cl-3afd1d56"><span class="cl-3afadf3c">2</span></p></th><th class="cl-3afd2850"><p class="cl-3afd1d56"><span class="cl-3afadf3c">3</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">0</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">BLQ</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">BLQ</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">BLQ</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">1</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">690</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">750</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">670</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">2</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">588</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">577</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">576</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">3</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">506</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">508</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">501</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">4</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">438</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">476</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">441</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">7</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">303</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">425</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">320</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">21</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">123</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">274</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">139</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">28</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">95.5</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">220</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">104</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd2851"><p class="cl-3afd1d56"><span class="cl-3afadf3c">35</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">75.8</span></p></td><td class="cl-3afd2852"><p class="cl-3afd1d56"><span class="cl-3afadf3c">177</span></p></td><td class="cl-3afd285a"><p class="cl-3afd1d56"><span class="cl-3afadf3c">77.8</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3afd285b"><p class="cl-3afd1d56"><span class="cl-3afadf3c">42</span></p></td><td class="cl-3afd285c"><p class="cl-3afd1d56"><span class="cl-3afadf3c">60.6</span></p></td><td class="cl-3afd285c"><p class="cl-3afd1d56"><span class="cl-3afadf3c">142</span></p></td><td class="cl-3afd285d"><p class="cl-3afd1d56"><span class="cl-3afadf3c">58.6</span></p></td></tr></tbody></table></div><span class="r-in"><span></span></span>
+<style></style><div class="tabwid"><style>.cl-9075793c{}.cl-90720266{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-90736700{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-907372ea{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-907372f4{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-907372fe{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-907372ff{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737300{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737308{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737309{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9073730a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737312{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737313{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737314{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9073731c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing="true" class="table cl-9075793c"><thead><tr style="overflow-wrap:break-word;"><th class="cl-907372ea"><p class="cl-90736700"><span class="cl-90720266">Time</span></p></th><th colspan="3" class="cl-907372f4"><p class="cl-90736700"><span class="cl-90720266">Concentration (ng/mL) by ID</span></p></th></tr><tr style="overflow-wrap:break-word;"><th class="cl-907372ff"><p class="cl-90736700"><span class="cl-90720266">(day)</span></p></th><th class="cl-90737300"><p class="cl-90736700"><span class="cl-90720266">1</span></p></th><th class="cl-90737300"><p class="cl-90736700"><span class="cl-90720266">2</span></p></th><th class="cl-90737308"><p class="cl-90736700"><span class="cl-90720266">3</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">0</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">BLQ</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">BLQ</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">BLQ</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">1</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">690</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">750</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">670</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">2</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">588</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">577</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">576</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">3</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">506</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">508</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">501</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">4</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">438</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">476</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">441</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">7</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">303</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">425</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">320</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">21</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">123</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">274</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">139</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">28</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">95.5</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">220</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">104</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737309"><p class="cl-90736700"><span class="cl-90720266">35</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">75.8</span></p></td><td class="cl-9073730a"><p class="cl-90736700"><span class="cl-90720266">177</span></p></td><td class="cl-90737312"><p class="cl-90736700"><span class="cl-90720266">77.8</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-90737313"><p class="cl-90736700"><span class="cl-90720266">42</span></p></td><td class="cl-90737314"><p class="cl-90736700"><span class="cl-90720266">60.6</span></p></td><td class="cl-90737314"><p class="cl-90736700"><span class="cl-90720266">142</span></p></td><td class="cl-9073731c"><p class="cl-90736700"><span class="cl-90720266">58.6</span></p></td></tr></tbody></table></div><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Current active figure:</span></span></span>
 <span class="r-in"><span><span class="va">res</span> <span class="op">=</span> <span class="fu">NCA_fetch_current_obj</span><span class="op">(</span><span class="va">state</span>, <span class="st">"figure"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">res</span><span class="op">$</span><span class="va">ggplot</span></span></span>
@@ -233,7 +233,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_data_format.html b/docs/reference/NCA_fetch_data_format.html
index e123c39..f6a14b7 100644
--- a/docs/reference/NCA_fetch_data_format.html
+++ b/docs/reference/NCA_fetch_data_format.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Use this to get information about data formats."><title>Fetches Details About Data Requirements — NCA_fetch_data_format • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Details About Data Requirements — NCA_fetch_data_format"><meta property="og:description" content="Use this to get information about data formats."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body>
@@ -130,7 +130,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/reference/NCA_fetch_ds.html b/docs/reference/NCA_fetch_ds.html
index 627e237..acb3fb9 100644
--- a/docs/reference/NCA_fetch_ds.html
+++ b/docs/reference/NCA_fetch_ds.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the datasets contained in the module"><title>Fetch Module Datasets — NCA_fetch_ds • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Datasets — NCA_fetch_ds"><meta property="og:description" content="Fetches the datasets contained in the module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the datasets contained in the module"><title>Fetch Module Datasets — NCA_fetch_ds • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch Module Datasets — NCA_fetch_ds"><meta property="og:description" content="Fetches the datasets contained in the module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -199,7 +199,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:5ccca71fdb91ecdaf857355530e8c73a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:52d5a2f8439af77e5e911817c5c4ec44</span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">myDs</span> <span class="op">=</span> <span class="fu">NCA_fetch_ds</span><span class="op">(</span><span class="va">state</span><span class="op">)</span></span></span>
@@ -214,7 +214,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_np_meta.html b/docs/reference/NCA_fetch_np_meta.html
index 2f07bb0..57186dd 100644
--- a/docs/reference/NCA_fetch_np_meta.html
+++ b/docs/reference/NCA_fetch_np_meta.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This provides meta information about NCA parameters. This
-includes parameter names, text descriptions, formatting (md and LaTeX)."><title>Fetches NCA Parameter Meta Information — NCA_fetch_np_meta • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches NCA Parameter Meta Information — NCA_fetch_np_meta"><meta property="og:description" content="This provides meta information about NCA parameters. This
+includes parameter names, text descriptions, formatting (md and LaTeX)."><title>Fetches NCA Parameter Meta Information — NCA_fetch_np_meta • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches NCA Parameter Meta Information — NCA_fetch_np_meta"><meta property="og:description" content="This provides meta information about NCA parameters. This
 includes parameter names, text descriptions, formatting (md and LaTeX)."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -999,7 +999,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_fetch_state.html b/docs/reference/NCA_fetch_state.html
index f832be3..0cdbdd9 100644
--- a/docs/reference/NCA_fetch_state.html
+++ b/docs/reference/NCA_fetch_state.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch ruminate State — NCA_fetch_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch ruminate State — NCA_fetch_state"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Merges default app options with the changes made in the UI"><title>Fetch ruminate State — NCA_fetch_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch ruminate State — NCA_fetch_state"><meta property="og:description" content="Merges default app options with the changes made in the UI"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -302,7 +302,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:e30844b54d209fd166d1a06622813fc3</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:a5bb5d7b20c96ab03e19eea14dc7dffe</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -363,7 +363,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_find_col.html b/docs/reference/NCA_find_col.html
index 913d63e..258b3d1 100644
--- a/docs/reference/NCA_find_col.html
+++ b/docs/reference/NCA_find_col.html
@@ -11,7 +11,7 @@
 The first match found is returned.
 If nothing is found then the first value of dscols is returned.
 
-'><title>Determines Default Column Name — NCA_find_col • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Determines Default Column Name — NCA_find_col"><meta property="og:description" content='Based on the current analysis, value from the UI, an optional
+'><title>Determines Default Column Name — NCA_find_col • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Determines Default Column Name — NCA_find_col"><meta property="og:description" content='Based on the current analysis, value from the UI, an optional
 list of patterns to search, and column names from a dataset this function
 tries to find a default value for a column in the analysis (e.g. subject id,
 dose, concentration, etc).
@@ -254,7 +254,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:e48e1227fb3ea9eb68a408c3016ff336</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:72ab0c616b09e538de2950b1c4b79238</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -315,7 +315,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_init_state.html b/docs/reference/NCA_init_state.html
index defa19f..b0efffe 100644
--- a/docs/reference/NCA_init_state.html
+++ b/docs/reference/NCA_init_state.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a list of the initialized module state"><title>Initialize NCA Module State — NCA_init_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize NCA Module State — NCA_init_state"><meta property="og:description" content="Creates a list of the initialized module state"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a list of the initialized module state"><title>Initialize NCA Module State — NCA_init_state • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize NCA Module State — NCA_init_state"><meta property="og:description" content="Creates a list of the initialized module state"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -125,7 +125,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_load_scenario.html b/docs/reference/NCA_load_scenario.html
index bbdab66..a07a386 100644
--- a/docs/reference/NCA_load_scenario.html
+++ b/docs/reference/NCA_load_scenario.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Loads a pre-defined analysis scneario from the NCA YAML config
-file."><title>Loads Pre-Defined Scenario — NCA_load_scenario • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Loads Pre-Defined Scenario — NCA_load_scenario"><meta property="og:description" content="Loads a pre-defined analysis scneario from the NCA YAML config
+file."><title>Loads Pre-Defined Scenario — NCA_load_scenario • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Loads Pre-Defined Scenario — NCA_load_scenario"><meta property="og:description" content="Loads a pre-defined analysis scneario from the NCA YAML config
 file."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -113,7 +113,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/reference/NCA_mkactive_ana.html b/docs/reference/NCA_mkactive_ana.html
index 6b053da..e115275 100644
--- a/docs/reference/NCA_mkactive_ana.html
+++ b/docs/reference/NCA_mkactive_ana.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the PKNCA output for a specified analysis"><title>Fetch PKNCA Results Object — NCA_mkactive_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch PKNCA Results Object — NCA_mkactive_ana"><meta property="og:description" content="Fetches the PKNCA output for a specified analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fetches the PKNCA output for a specified analysis"><title>Fetch PKNCA Results Object — NCA_mkactive_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetch PKNCA Results Object — NCA_mkactive_ana"><meta property="og:description" content="Fetches the PKNCA output for a specified analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -203,7 +203,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:b269fe481810a61478d61c29538bda42</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:e4497a31c610e31ca6e3073180d6bc1d</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -264,7 +264,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_new_ana.html b/docs/reference/NCA_new_ana.html
index 5d473c0..11a0075 100644
--- a/docs/reference/NCA_new_ana.html
+++ b/docs/reference/NCA_new_ana.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a new NCA analysis in an NCA module"><title>Initialize New Analysis — NCA_new_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Initialize New Analysis — NCA_new_ana"><meta property="og:description" content="Creates a new NCA analysis in an NCA module"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body>
@@ -199,7 +199,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:b972ae6a0a3e49d3e4952575f68ee62c</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:0c11176cc0bc70425aeb3da2bf2a9264</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -260,7 +260,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_process_current_ana.html b/docs/reference/NCA_process_current_ana.html
index 6969c58..ef6cf99 100644
--- a/docs/reference/NCA_process_current_ana.html
+++ b/docs/reference/NCA_process_current_ana.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the current analysis and checks different aspects to for
-any issues to make sure it's good to go."><title>Processes Current Analysis to be Run — NCA_process_current_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Processes Current Analysis to be Run — NCA_process_current_ana"><meta property="og:description" content="Takes the current analysis and checks different aspects to for
+any issues to make sure it's good to go."><title>Processes Current Analysis to be Run — NCA_process_current_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Processes Current Analysis to be Run — NCA_process_current_ana"><meta property="og:description" content="Takes the current analysis and checks different aspects to for
 any issues to make sure it's good to go."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -201,7 +201,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:90c7e0468c17f9b7317106fa5207b923</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:dfc696548b791a5d407726a6bde87421</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -262,7 +262,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_set_current_ana.html b/docs/reference/NCA_set_current_ana.html
index 258f4ba..95f098a 100644
--- a/docs/reference/NCA_set_current_ana.html
+++ b/docs/reference/NCA_set_current_ana.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes an NCA state and an analysis list and sets that figure list
-as the value for the active figure"><title>Sets Current Analysis — NCA_set_current_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Sets Current Analysis — NCA_set_current_ana"><meta property="og:description" content="Takes an NCA state and an analysis list and sets that figure list
+as the value for the active figure"><title>Sets Current Analysis — NCA_set_current_ana • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Sets Current Analysis — NCA_set_current_ana"><meta property="og:description" content="Takes an NCA state and an analysis list and sets that figure list
 as the value for the active figure"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -205,7 +205,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:bcad862065667dade418900e45c043b6</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:5baf846cbf57819d7602dcc87f9499d2</span>
 <span class="r-in"><span>                                                                               </span></span>
 <span class="r-in"><span><span class="co"># Extracting the session and input variables                                   </span></span></span>
 <span class="r-in"><span><span class="va">session</span>      <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">session</span>                                                </span></span>
@@ -266,7 +266,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/NCA_test_mksession.html b/docs/reference/NCA_test_mksession.html
index 4ae4b6f..1973da6 100644
--- a/docs/reference/NCA_test_mksession.html
+++ b/docs/reference/NCA_test_mksession.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Populates the supplied session variable for testing."><title>Populate Session Data for Module Testing — NCA_test_mksession • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Populate Session Data for Module Testing — NCA_test_mksession"><meta property="og:description" content="Populates the supplied session variable for testing."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Populates the supplied session variable for testing."><title>Populate Session Data for Module Testing — NCA_test_mksession • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Populate Session Data for Module Testing — NCA_test_mksession"><meta property="og:description" content="Populates the supplied session variable for testing."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -217,7 +217,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:041729aa6c32af204b5b09837ba504b6</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:205e69e2051b7154e4a5792e4a97a35a</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -229,7 +229,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
index cde7d88..c4aa9c6 100644
Binary files a/docs/reference/Rplot001.png and b/docs/reference/Rplot001.png differ
diff --git a/docs/reference/apply_route_map.html b/docs/reference/apply_route_map.html
index f36a39f..d1d7d1e 100644
--- a/docs/reference/apply_route_map.html
+++ b/docs/reference/apply_route_map.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Used to convert nonstandard dose route values (i.e. "IV") to
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+standard values ("intravascular").'><title>Applies Route Mapping to Dataset — apply_route_map • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Applies Route Mapping to Dataset — apply_route_map"><meta property="og:description" content='Used to convert nonstandard dose route values (i.e. "IV") to
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diff --git a/docs/reference/dose_records_builder.html b/docs/reference/dose_records_builder.html
index 22f0d2d..789aba1 100644
--- a/docs/reference/dose_records_builder.html
+++ b/docs/reference/dose_records_builder.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes information about columns in dataset and constructs the
-dosing records."><title>Builds Dose Records Dataframe — dose_records_builder • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Builds Dose Records Dataframe — dose_records_builder"><meta property="og:description" content="Takes information about columns in dataset and constructs the
+dosing records."><title>Builds Dose Records Dataframe — dose_records_builder • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Builds Dose Records Dataframe — dose_records_builder"><meta property="og:description" content="Takes information about columns in dataset and constructs the
 dosing records."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -231,7 +231,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/reference/fetch_rxinfo-1.png b/docs/reference/fetch_rxinfo-1.png
index d52301b..40e7643 100644
Binary files a/docs/reference/fetch_rxinfo-1.png and b/docs/reference/fetch_rxinfo-1.png differ
diff --git a/docs/reference/fetch_rxinfo.html b/docs/reference/fetch_rxinfo.html
index 6d27193..3606d2e 100644
--- a/docs/reference/fetch_rxinfo.html
+++ b/docs/reference/fetch_rxinfo.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This will provide information like parameter names, covriates,
-etc from an rxode2 object."><title>Fetches Information from an rxode2 Object — fetch_rxinfo • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Information from an rxode2 Object — fetch_rxinfo"><meta property="og:description" content="This will provide information like parameter names, covriates,
+etc from an rxode2 object."><title>Fetches Information from an rxode2 Object — fetch_rxinfo • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Information from an rxode2 Object — fetch_rxinfo"><meta property="og:description" content="This will provide information like parameter names, covriates,
 etc from an rxode2 object."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -106,8 +106,8 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://formods.ubiquity.tools/" class="external-link">formods</a></span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading formods</span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>package ‘ggplot2’ was built under R version 4.2.3</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># For more information see the Clinical Trial Simulation vignette:</span></span></span>
 <span class="r-in"><span><span class="co"># https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html</span></span></span>
@@ -254,7 +254,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/fetch_rxtc.html b/docs/reference/fetch_rxtc.html
index 31bf60c..f0e2b96 100644
--- a/docs/reference/fetch_rxtc.html
+++ b/docs/reference/fetch_rxtc.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the output of rxSolve() and merges in any missing
-covariates that are present in params but not in sim"><title>Extracts Timecourse and Merges Covariates — fetch_rxtc • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extracts Timecourse and Merges Covariates — fetch_rxtc"><meta property="og:description" content="Takes the output of rxSolve() and merges in any missing
+covariates that are present in params but not in sim"><title>Extracts Timecourse and Merges Covariates — fetch_rxtc • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extracts Timecourse and Merges Covariates — fetch_rxtc"><meta property="og:description" content="Takes the output of rxSolve() and merges in any missing
 covariates that are present in params but not in sim"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -112,7 +112,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/index.html b/docs/reference/index.html
index c97cae5..3993335 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function reference • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function reference"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -497,7 +497,7 @@ <h2 id="nca-module-functions">NCA Module functions<a class="anchor" aria-label="
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/mk_figure_ind_obs-1.png b/docs/reference/mk_figure_ind_obs-1.png
index e3c88ff..8dbb4b2 100644
Binary files a/docs/reference/mk_figure_ind_obs-1.png and b/docs/reference/mk_figure_ind_obs-1.png differ
diff --git a/docs/reference/mk_figure_ind_obs.html b/docs/reference/mk_figure_ind_obs.html
index 34d005c..99c9771 100644
--- a/docs/reference/mk_figure_ind_obs.html
+++ b/docs/reference/mk_figure_ind_obs.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the output of PKNCA and creates ggplot figures faceted
-by subject id highlighting of certain NCA aspects (e.g. points used for half-life)"><title>Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs"><meta property="og:description" content="Takes the output of PKNCA and creates ggplot figures faceted
+by subject id highlighting of certain NCA aspects (e.g. points used for half-life)"><title>Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs"><meta property="og:description" content="Takes the output of PKNCA and creates ggplot figures faceted
 by subject id highlighting of certain NCA aspects (e.g. points used for half-life)"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -250,7 +250,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:19f16a93834ab4eb3b18d984ccca5b55</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:aa766d3936aae9435fc7d2cb3db4cdf7</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
@@ -275,7 +275,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/mk_rx_obj.html b/docs/reference/mk_rx_obj.html
index 3224fd0..74bf7e0 100644
--- a/docs/reference/mk_rx_obj.html
+++ b/docs/reference/mk_rx_obj.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates an rxode2 object from a model (either rxode2 function
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+or a NONMEM file)"><title>Makes an rxode2 Object — mk_rx_obj • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Makes an rxode2 Object — mk_rx_obj"><meta property="og:description" content="Creates an rxode2 object from a model (either rxode2 function
 or a NONMEM file)"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -172,7 +172,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span>         cp ~ prop(propSd)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x13840ea00&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x140b10510&gt;</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># rxode2 object</span></span></span>
 <span class="r-in"><span><span class="va">rx_res</span><span class="op">[[</span><span class="st">"capture"</span><span class="op">]</span><span class="op">]</span><span class="op">[[</span><span class="st">"rx_obj"</span><span class="op">]</span><span class="op">]</span></span></span>
@@ -213,7 +213,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/mk_subjects-1.png b/docs/reference/mk_subjects-1.png
index d52301b..40e7643 100644
Binary files a/docs/reference/mk_subjects-1.png and b/docs/reference/mk_subjects-1.png differ
diff --git a/docs/reference/mk_subjects.html b/docs/reference/mk_subjects.html
index b272503..c4032a7 100644
--- a/docs/reference/mk_subjects.html
+++ b/docs/reference/mk_subjects.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This will provide information like parameter names, covriates,
-etc from an rxode2 object."><title>Fetches Information from an rxode2 Object — mk_subjects • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Information from an rxode2 Object — mk_subjects"><meta property="og:description" content="This will provide information like parameter names, covriates,
+etc from an rxode2 object."><title>Fetches Information from an rxode2 Object — mk_subjects • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fetches Information from an rxode2 Object — mk_subjects"><meta property="og:description" content="This will provide information like parameter names, covriates,
 etc from an rxode2 object."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -392,7 +392,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/mk_table_ind_obs.html b/docs/reference/mk_table_ind_obs.html
index 187b3cc..9d1a4ca 100644
--- a/docs/reference/mk_table_ind_obs.html
+++ b/docs/reference/mk_table_ind_obs.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the output of PKNCA and creates a tabular view of the
 individual observation data. This can be spread out of over several tables
-(pages) if necessary."><title>Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs"><meta property="og:description" content="Takes the output of PKNCA and creates a tabular view of the
+(pages) if necessary."><title>Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs"><meta property="og:description" content="Takes the output of PKNCA and creates a tabular view of the
 individual observation data. This can be spread out of over several tables
 (pages) if necessary."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -268,7 +268,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:1f720c87d439d2ce9fb19753e27cd676</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:f1d2ff38b4c9fc2abdcc109d17292a24</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
@@ -281,7 +281,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Building the figure</span></span></span>
 <span class="r-in"><span><span class="va">mk_res</span> <span class="op">=</span> <span class="fu">mk_table_ind_obs</span><span class="op">(</span>nca_res <span class="op">=</span> <span class="va">pknca_res</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">mk_res</span><span class="op">$</span><span class="va">tables</span><span class="op">[[</span><span class="st">"Table 1"</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">ft</span></span></span>
-<style></style><div class="tabwid"><style>.cl-4620932a{}.cl-461dc6f4{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-461ee94e{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-461ef182{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef183{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef18c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef18d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef196{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef197{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef198{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1a0{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1a1{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1aa{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1ab{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1b4{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing="true" class="table cl-4620932a"><thead><tr style="overflow-wrap:break-word;"><th class="cl-461ef182"><p class="cl-461ee94e"><span class="cl-461dc6f4">Time</span></p></th><th colspan="3" class="cl-461ef183"><p class="cl-461ee94e"><span class="cl-461dc6f4">Concentration (ng/mL) by ID</span></p></th></tr><tr style="overflow-wrap:break-word;"><th class="cl-461ef18d"><p class="cl-461ee94e"><span class="cl-461dc6f4">(day)</span></p></th><th class="cl-461ef196"><p class="cl-461ee94e"><span class="cl-461dc6f4">1</span></p></th><th class="cl-461ef196"><p class="cl-461ee94e"><span class="cl-461dc6f4">2</span></p></th><th class="cl-461ef197"><p class="cl-461ee94e"><span class="cl-461dc6f4">3</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">0</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">BLQ</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">BLQ</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">BLQ</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">1</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">690</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">750</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">670</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">2</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">588</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">577</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">576</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">3</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">506</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">508</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">501</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">4</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">438</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">476</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">441</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">7</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">303</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">425</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">320</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">21</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">123</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">274</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">139</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">28</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">95.5</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">220</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">104</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef198"><p class="cl-461ee94e"><span class="cl-461dc6f4">35</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">75.8</span></p></td><td class="cl-461ef1a0"><p class="cl-461ee94e"><span class="cl-461dc6f4">177</span></p></td><td class="cl-461ef1a1"><p class="cl-461ee94e"><span class="cl-461dc6f4">77.8</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-461ef1aa"><p class="cl-461ee94e"><span class="cl-461dc6f4">42</span></p></td><td class="cl-461ef1ab"><p class="cl-461ee94e"><span class="cl-461dc6f4">60.6</span></p></td><td class="cl-461ef1ab"><p class="cl-461ee94e"><span class="cl-461dc6f4">142</span></p></td><td class="cl-461ef1b4"><p class="cl-461ee94e"><span class="cl-461dc6f4">58.6</span></p></td></tr></tbody></table></div></code></pre></div>
+<style></style><div class="tabwid"><style>.cl-9c77c794{}.cl-9c74b2ac{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-9c75d86c{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-9c75e0c8{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0d2{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0d3{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0d4{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0dc{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0e6{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0e7{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0e8{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0f0{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0f1{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0f2{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0fa{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing="true" class="table cl-9c77c794"><thead><tr style="overflow-wrap:break-word;"><th class="cl-9c75e0c8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">Time</span></p></th><th colspan="3" class="cl-9c75e0d2"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">Concentration (ng/mL) by ID</span></p></th></tr><tr style="overflow-wrap:break-word;"><th class="cl-9c75e0d4"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">(day)</span></p></th><th class="cl-9c75e0dc"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">1</span></p></th><th class="cl-9c75e0dc"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">2</span></p></th><th class="cl-9c75e0e6"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">3</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">0</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">BLQ</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">BLQ</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">BLQ</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">1</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">690</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">750</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">670</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">2</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">588</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">577</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">576</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">3</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">506</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">508</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">501</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">4</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">438</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">476</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">441</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">7</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">303</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">425</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">320</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">21</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">123</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">274</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">139</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">28</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">95.5</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">220</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">104</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0e7"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">35</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">75.8</span></p></td><td class="cl-9c75e0e8"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">177</span></p></td><td class="cl-9c75e0f0"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">77.8</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9c75e0f1"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">42</span></p></td><td class="cl-9c75e0f2"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">60.6</span></p></td><td class="cl-9c75e0f2"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">142</span></p></td><td class="cl-9c75e0fa"><p class="cl-9c75d86c"><span class="cl-9c74b2ac">58.6</span></p></td></tr></tbody></table></div></code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -292,7 +292,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/mk_table_nca_params.html b/docs/reference/mk_table_nca_params.html
index 5c971e1..2101b3b 100644
--- a/docs/reference/mk_table_nca_params.html
+++ b/docs/reference/mk_table_nca_params.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Create paginated tables from PKNCA to use in reports and Shiny
-apps."><title>Create Tabular Output from PKNCA Results — mk_table_nca_params • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create Tabular Output from PKNCA Results — mk_table_nca_params"><meta property="og:description" content="Create paginated tables from PKNCA to use in reports and Shiny
+apps."><title>Create Tabular Output from PKNCA Results — mk_table_nca_params • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create Tabular Output from PKNCA Results — mk_table_nca_params"><meta property="og:description" content="Create paginated tables from PKNCA to use in reports and Shiny
 apps."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -284,7 +284,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:bbad68490dee0aaebcd3b23b4936bb58</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:951d3cc4bcfe04cbdd51235b5d3cb130</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
@@ -300,7 +300,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Building the figure</span></span></span>
 <span class="r-in"><span><span class="va">mk_res</span> <span class="op">=</span> <span class="fu">mk_table_nca_params</span><span class="op">(</span>nca_res <span class="op">=</span> <span class="va">pknca_res</span>, nps<span class="op">=</span><span class="va">nps</span>, digits<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">mk_res</span><span class="op">$</span><span class="va">tables</span><span class="op">[[</span><span class="st">"Table 1"</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">ft</span></span></span>
-<style></style><div class="tabwid"><style>.cl-46faa524{}.cl-46f7ca66{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-46f8e7ac{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-46f8f102{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f10c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f10d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f116{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f117{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f118{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f119{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f120{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f121{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f12a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f12b{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f12c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f134{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f135{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing="true" class="table cl-46faa524"><thead><tr style="overflow-wrap:break-word;"><th class="cl-46f8f102"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">ID</span></p></th><th class="cl-46f8f10c"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">AUClast</span></p></th><th class="cl-46f8f10c"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">Cmax</span></p></th><th class="cl-46f8f10d"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">AUCinf,obs</span></p></th></tr><tr style="overflow-wrap:break-word;"><th rowspan="2" class="cl-46f8f116"><p class="cl-46f8e7ac"><span class="cl-46f7ca66"></span></p></th><th class="cl-46f8f117"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">day*ng/mL</span></p></th><th class="cl-46f8f117"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">ng/mL</span></p></th><th class="cl-46f8f118"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">day*ng/mL</span></p></th></tr><tr style="overflow-wrap:break-word;"><th colspan="2" class="cl-46f8f119"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">[0,21]</span></p></th><th class="cl-46f8f120"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">[0,inf]</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-46f8f121"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">1</span></p></td><td class="cl-46f8f12a"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">5,900</span></p></td><td class="cl-46f8f12a"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">690</span></p></td><td class="cl-46f8f12b"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">9,590</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-46f8f121"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">2</span></p></td><td class="cl-46f8f12a"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">8,240</span></p></td><td class="cl-46f8f12a"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">750</span></p></td><td class="cl-46f8f12b"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">17,000</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-46f8f12c"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">3</span></p></td><td class="cl-46f8f134"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">6,140</span></p></td><td class="cl-46f8f134"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">670</span></p></td><td class="cl-46f8f135"><p class="cl-46f8e7ac"><span class="cl-46f7ca66">9,520</span></p></td></tr></tbody></table></div></code></pre></div>
+<style></style><div class="tabwid"><style>.cl-9d50e100{}.cl-9d4dfa8a{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-9d4f0ccc{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-9d4f146a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f146b{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1474{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f147e{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f147f{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1480{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1488{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1492{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1493{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f149c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f149d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f14a6{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f14a7{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f14a8{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing="true" class="table cl-9d50e100"><thead><tr style="overflow-wrap:break-word;"><th class="cl-9d4f146a"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">ID</span></p></th><th class="cl-9d4f146b"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">AUClast</span></p></th><th class="cl-9d4f146b"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">Cmax</span></p></th><th class="cl-9d4f1474"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">AUCinf,obs</span></p></th></tr><tr style="overflow-wrap:break-word;"><th rowspan="2" class="cl-9d4f147e"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a"></span></p></th><th class="cl-9d4f147f"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">day*ng/mL</span></p></th><th class="cl-9d4f147f"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">ng/mL</span></p></th><th class="cl-9d4f1480"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">day*ng/mL</span></p></th></tr><tr style="overflow-wrap:break-word;"><th colspan="2" class="cl-9d4f1488"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">[0,21]</span></p></th><th class="cl-9d4f1492"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">[0,inf]</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-9d4f1493"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">1</span></p></td><td class="cl-9d4f149c"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">5,900</span></p></td><td class="cl-9d4f149c"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">690</span></p></td><td class="cl-9d4f149d"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">9,590</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9d4f1493"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">2</span></p></td><td class="cl-9d4f149c"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">8,240</span></p></td><td class="cl-9d4f149c"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">750</span></p></td><td class="cl-9d4f149d"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">17,000</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-9d4f14a6"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">3</span></p></td><td class="cl-9d4f14a7"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">6,140</span></p></td><td class="cl-9d4f14a7"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">670</span></p></td><td class="cl-9d4f14a8"><p class="cl-9d4f0ccc"><span class="cl-9d4dfa8a">9,520</span></p></td></tr></tbody></table></div></code></pre></div>
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@@ -311,7 +311,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/docs/reference/nca_builder.html b/docs/reference/nca_builder.html
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@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the current analysis in the state object and creates the
-code to run the analysis"><title>Builds NCA Code from ui Elements — nca_builder • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Builds NCA Code from ui Elements — nca_builder"><meta property="og:description" content="Takes the current analysis in the state object and creates the
+code to run the analysis"><title>Builds NCA Code from ui Elements — nca_builder • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Builds NCA Code from ui Elements — nca_builder"><meta property="og:description" content="Takes the current analysis in the state object and creates the
 code to run the analysis"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -202,7 +202,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:14126a9100b4aee25d0c555032bf1ca4</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:9a77ea921271e7e3fa800559c31869f6</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
@@ -227,7 +227,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/plot_sr_ev-1.png b/docs/reference/plot_sr_ev-1.png
index d52301b..40e7643 100644
Binary files a/docs/reference/plot_sr_ev-1.png and b/docs/reference/plot_sr_ev-1.png differ
diff --git a/docs/reference/plot_sr_ev.html b/docs/reference/plot_sr_ev.html
index 74e468d..1ba2274 100644
--- a/docs/reference/plot_sr_ev.html
+++ b/docs/reference/plot_sr_ev.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="   Plots the timecourse of `simulate_rules()` output.
-"><title>Plots        Timecourse of Rules Simulations — plot_sr_ev • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plots        Timecourse of Rules Simulations — plot_sr_ev"><meta property="og:description" content="   Plots the timecourse of `simulate_rules()` output.
+"><title>Plots        Timecourse of Rules Simulations — plot_sr_ev • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plots        Timecourse of Rules Simulations — plot_sr_ev"><meta property="og:description" content="   Plots the timecourse of `simulate_rules()` output.
 "><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -303,7 +303,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/plot_sr_tc-1.png b/docs/reference/plot_sr_tc-1.png
index d52301b..40e7643 100644
Binary files a/docs/reference/plot_sr_tc-1.png and b/docs/reference/plot_sr_tc-1.png differ
diff --git a/docs/reference/plot_sr_tc.html b/docs/reference/plot_sr_tc.html
index 959c6e1..928da3d 100644
--- a/docs/reference/plot_sr_tc.html
+++ b/docs/reference/plot_sr_tc.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="   Plots the timecourse of `simulate_rules()` output.
-"><title>Plots        Timecourse of Rules Simulations — plot_sr_tc • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plots        Timecourse of Rules Simulations — plot_sr_tc"><meta property="og:description" content="   Plots the timecourse of `simulate_rules()` output.
+"><title>Plots        Timecourse of Rules Simulations — plot_sr_tc • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plots        Timecourse of Rules Simulations — plot_sr_tc"><meta property="og:description" content="   Plots the timecourse of `simulate_rules()` output.
 "><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -303,7 +303,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
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diff --git a/docs/reference/ruminate.html b/docs/reference/ruminate.html
index b7429af..3f2cfea 100644
--- a/docs/reference/ruminate.html
+++ b/docs/reference/ruminate.html
@@ -5,7 +5,7 @@
 Word or PowerPoint. The state of the app can be saved and loaded at a later
 date. When saved, a script is generated to reproduce the different actions in
 the Shiny interface.
-Runs the pharmacometrics ruminate app."><title>ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate"><meta property="og:description" content="This is done by creating a Shiny interface to different tools for data
+Runs the pharmacometrics ruminate app."><title>ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate"><meta property="og:description" content="This is done by creating a Shiny interface to different tools for data
 transformation (dplyr and tidyr), plotting (ggplot2), and
 noncompartmental analysis (PKNCA). These results can be reported in Excel,
 Word or PowerPoint. The state of the app can be saved and loaded at a later
@@ -158,7 +158,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/ruminate_check.html b/docs/reference/ruminate_check.html
index c866a70..0918d4c 100644
--- a/docs/reference/ruminate_check.html
+++ b/docs/reference/ruminate_check.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Looks at the suggested dependencies and checks to make sure
-they are installed."><title>Checks ruminate Dependencies — ruminate_check • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Checks ruminate Dependencies — ruminate_check"><meta property="og:description" content="Looks at the suggested dependencies and checks to make sure
+they are installed."><title>Checks ruminate Dependencies — ruminate_check • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Checks ruminate Dependencies — ruminate_check"><meta property="og:description" content="Looks at the suggested dependencies and checks to make sure
 they are installed."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -129,7 +129,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/run_nca_components.html b/docs/reference/run_nca_components.html
index 8a24931..5f5b15d 100644
--- a/docs/reference/run_nca_components.html
+++ b/docs/reference/run_nca_components.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes the current state and runs the current analysis in that
-state."><title>Runs NCA for the Current Analysis — run_nca_components • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Runs NCA for the Current Analysis — run_nca_components"><meta property="og:description" content="Takes the current state and runs the current analysis in that
+state."><title>Runs NCA for the Current Analysis — run_nca_components • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Runs NCA for the Current Analysis — run_nca_components"><meta property="og:description" content="Takes the current state and runs the current analysis in that
 state."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -206,7 +206,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:1aa0507d8b25ad9c39353070c684066a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:53d39c9297f09f7aea6cbfe0c1583966</span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="va">sess_res</span><span class="op">$</span><span class="va">state</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">=</span> <span class="fu">run_nca_components</span><span class="op">(</span><span class="va">state</span>, components<span class="op">=</span><span class="st">"tb_ind_params"</span><span class="op">)</span></span></span>
@@ -219,7 +219,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:1aa0507d8b25ad9c39353070c684066a</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> → NCA: module checksum updated:53d39c9297f09f7aea6cbfe0c1583966</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -231,7 +231,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/simulate_rules-1.png b/docs/reference/simulate_rules-1.png
index d52301b..40e7643 100644
Binary files a/docs/reference/simulate_rules-1.png and b/docs/reference/simulate_rules-1.png differ
diff --git a/docs/reference/simulate_rules.html b/docs/reference/simulate_rules.html
index 8bd94fd..f73ceb5 100644
--- a/docs/reference/simulate_rules.html
+++ b/docs/reference/simulate_rules.html
@@ -2,7 +2,7 @@
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Simulate an rxode2 model based on rules evaluated at specified
 time-points. For example if you want to titrate dosing based on individual
 plasma levels you could create a rule that changes dosing at specified time
-points based on the last observation of the user."><title>Rule-Based simulates — simulate_rules • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Rule-Based simulates — simulate_rules"><meta property="og:description" content="Simulate an rxode2 model based on rules evaluated at specified
+points based on the last observation of the user."><title>Rule-Based simulates — simulate_rules • ruminate</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Rule-Based simulates — simulate_rules"><meta property="og:description" content="Simulate an rxode2 model based on rules evaluated at specified
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-[{"path":"/articles/clinical_trial_simulation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Clinical Trial Simulation","text":"Normally want simulate patients response define regimen just simulate forward specified point time. works well compounds relatively safe, PK consistent time, drug concentrations required achieve efficacy consistent. fact restrictions apply many different compounds. However, many scenarios necessary adjust dosing based periodic clinical visits: compounds narrow therapeutic index, disease progression alters drug PK requires increases dose amount, etc. possible manually current simulation tools provided, section outlines generalized framework make easier users.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Clinical Trial Simulation","text":"underlying simulations run using rxode2, need rxode2 system object. can either simulate subjects load file. Next need define set rules. set conditions actions. evaluation time point conditions evaluated. condition met actions associated condition executed. example, visit (evaluation time point) trough PK certain level (condition) may want increase dosing regimen next dosing cycle (action).","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"creating-subjects","dir":"Articles","previous_headings":"Overview","what":"Creating subjects","title":"Clinical Trial Simulation","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"covariates","dir":"Articles","previous_headings":"Overview > Creating subjects","what":"Covariates","title":"Clinical Trial Simulation","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/articles/clinical_trial_simulation.html","id":"rule-based-simulations","dir":"Articles","previous_headings":"Overview","what":"Rule-based simulations","title":"Clinical Trial Simulation","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"rules","dir":"Articles","previous_headings":"Overview > Rule-based simulations","what":"Rules","title":"Clinical Trial Simulation","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"rule-evaluation-environment","dir":"Articles","previous_headings":"Overview > Rule-based simulations","what":"Rule-evaluation environment","title":"Clinical Trial Simulation","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"the-following-functions-will-be-available","dir":"Articles","previous_headings":"Overview > Rule-based simulations > Rule-evaluation environment","what":"The following functions will be available:","title":"Clinical Trial Simulation","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":"/articles/clinical_trial_simulation.html","id":"example-fixed-dosing","dir":"Articles","previous_headings":"","what":"Example: Fixed dosing","title":"Clinical Trial Simulation","text":"explore framework simple system describing antibody PK driving change biomarker indirect response used. timescale model days system shown bottom want look . can also find {rumiante} package :","code":"system.file(package=\"ruminate\", \"test_apps\", \"test_rxode2_system.R\")"},{"path":"/articles/clinical_trial_simulation.html","id":"creating-the-rxode2-object","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Creating the rxode2 object","title":"Clinical Trial Simulation","text":"source example file create function my_model. can convert rxode2 object:","code":"source(system.file(package=\"ruminate\", \"test_apps\", \"test_rxode2_system.R\")) object = rxode2(my_model)"},{"path":"/articles/clinical_trial_simulation.html","id":"defining-subjects","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Defining subjects","title":"Clinical Trial Simulation","text":"first thing want create subjects. can using interindividual variability system. systems covariates need define . example three covariates needed. list create sampling SEX_ID randomly discrete distribution, fixing SUBTYPE_ID 0, sampling WT log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) ) set.seed(8675309) rxode2::rxSetSeed(8675309) subs = mk_subjects(object = object,                    nsub   = 6,                    covs   = covs) ##   id TV_F1 TV_ka     TV_CL    TV_Vc    TV_Vp       TV_Q TV_MW TV_BM_IC ## 1  1 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 2  2 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 3  3 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 4  4 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 5  5 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 6  6 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ##   TV_kdeg_BM TV_Emax TV_EC50       ETAka      ETACL      ETAVc      ETAVp ## 1        0.1     100     300  1.21921403 -0.2009231 -0.3384823  0.1217864 ## 2        0.1     100     300 -1.11242836 -0.1640987 -0.1598991  0.1466910 ## 3        0.1     100     300 -0.06807962 -0.3341488 -0.5686558 -0.1215476 ## 4        0.1     100     300 -0.44491868  0.6433960  0.2223175  0.5390626 ## 5        0.1     100     300 -0.44409456 -0.4895307 -0.5000220 -0.1807666 ## 6        0.1     100     300 -0.15464546 -0.4426699 -0.3959970 -0.3404295 ##         ETAQ    ETABM_IC    ETAEmax SEX_ID SUBTYPE_ID       WT ## 1  0.3975154  0.23930808  0.1464248      1          0 53.65521 ## 2  2.1129804  0.45798783 -0.1063589      0          0 88.42572 ## 3 -0.8616051  0.36654199 -0.3259271      1          0 65.97745 ## 4  0.5297385  0.27421551  0.1487639      1          0 58.48501 ## 5 -0.5466695  0.17227432 -0.1335189      1          0 63.43319 ## 6  1.3824647 -0.03320645 -0.2711974      1          0 82.05225"},{"path":"/articles/clinical_trial_simulation.html","id":"creating-rules","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Creating rules","title":"Clinical Trial Simulation","text":"rules named list. list names arbitrary used track going . list two required elements. condition action. condition character string evaluated results either TRUE FALSE. action list type list elements based type. Shown \"dose\" type action: can optionally add true_value false_value stored simulated output indicate evaluation status condition. setting dose type need also provide elements values, times, durations. character objects can evaluated provide vectors. ’s important note following providing dose type action: lengths equal. Times relative evaluation time point.","code":"rules = list(   low_dose = list(     condition = \"TRUE\",     true_value  = \"3mg\",     false_value = \"0\",     action    = list(       type  = \"dose\",       state     = \"Ac\",        values    = \"c(3,  3,  3,  3)*1e6/MW\",       times     = \"c(0, 14, 28, 42)\",       durations = \"c(0,  0,  0,  0)\")     )  )"},{"path":"/articles/clinical_trial_simulation.html","id":"evaluation-times","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Evaluation times","title":"Clinical Trial Simulation","text":"subjects rules defined need define timepoints rules evaluated. case considering 7 cycles (0 6) 8 weeks (2*28) .","code":"eval_times =  (0:6)*28*2"},{"path":"/articles/clinical_trial_simulation.html","id":"running-simulations","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Running simulations","title":"Clinical Trial Simulation","text":"define output times want simulation. Note last evlation point day 336 (6*2*8), follow timecourse one cycle day 392 (7*2*8). want pass options rxode2 can creating named list option name value paired together: Lastly can passed simulate_rules():","code":"output_times = seq(0, 7*28*2, 10) rx_options = list(    covsInterpolation = \"locf\",    addCov=TRUE ) simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":"/articles/clinical_trial_simulation.html","id":"example-resetting-states","dir":"Articles","previous_headings":"","what":"Example Resetting states","title":"Clinical Trial Simulation","text":"action type \"set state\" can used arbitrarily set value state system. setting central compartment value 10 time zero. time 56 increase value peripheral compartment Cp factor 5.","code":"rules = list(   reset_Ac    = list(    condition = \"time == 0\",    true_flag  = \"Ac set\",    false_flag = \"\",    action    = list(      type     = \"set state\",      state    = \"Ac\",      value    = \"10\")    ),  reset_Cp    = list(    condition = \"time == 56\",    true_flag  = \"Cp set\",    false_flag = \"\",    action    = list(      type     = \"set state\",      state    = \"Cp\",      value    = \"Cp*5\")   ) )  simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":"/articles/clinical_trial_simulation.html","id":"example-manual-code-evaluation","dir":"Articles","previous_headings":"","what":"Example: Manual code evaluation","title":"Clinical Trial Simulation","text":"find need something complicated can include functions preamble use functions methods . Alternatively can create code use \"manual\" rule type modify event table using objects rule evaluation environment . code simply setting state current subject id value.","code":"code=\" SI_interval_ev = etRbind(SI_interval_ev, et(cmt  = 'Ac',    id   = id,    amt  = 10,    evid = 4,    time = time))\"  rules = list(   manual_example = list(     condition = \"time == 56\",     true_flag  = \"manual\",     false_flag = \"\",     action    = list(       type  = \"manual\",       code      = code     )   ) )  simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":"/articles/clinical_trial_simulation.html","id":"example-titrated-dosing-of-a-biomarker","dir":"Articles","previous_headings":"","what":"Example: Titrated dosing of a biomarker","title":"Clinical Trial Simulation","text":"examples demonstrate mechanics perform rule-based simualtions. example provide concrete demonstration use elements together. want titrate dosing biomarker within range. range want maintain dose. first rule active time 0 initialize dosing 0.1 mg. remaining rules active first dose time 0 biomarker range (ss_dose_increase ), range (ss_dose_decrease), range (ss_dose_keep). use simulation internal function SI_fpd fetch previous dose select new dose fraction previous. need can create functions use action fields. just need create character string function definitions pass preamble input simulate_rules().","code":"rules = list(  first_cycle = list(    condition = \"time == 0\",    true_flag  = \"first cycle\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c(0.1,  0.1,  0.1,  0.1)*1e6/MW\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) ,  ss_dose_keep         = list(    condition = \"((BM <=  7e4) & (BM >=5e4)) & (time > 0)\",    true_flag  = \"keep last\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c( 1.0,  1.00,  1.00,  1.00)*SI_fpd(id=id, state='Ac')\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) ,  ss_dose_decrease     = list(    condition = \"(BM >  7e4) & (time > 0)\",    true_flag  = \"titrate down\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c( .90,   .90,   .90,   .90)*SI_fpd(id=id, state='Ac')\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) ,  ss_dose_increase = list(    condition = \"(BM <  5e4) & (time > 0)\",    true_flag  = \"titrate up\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c(1.30,  1.30,  1.30,  1.30)*SI_fpd(id=id, state='Ac')\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) )  simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":[]},{"path":[]},{"path":[]},{"path":"/articles/clinical_trial_simulation.html","id":"antibody-pk-and-biomarker-system","dir":"Articles","previous_headings":"","what":"Antibody-PK and biomarker system","title":"Clinical Trial Simulation","text":"","code":"# Dosing into Ac and At is in nmoles. So to dose 3 mg you would use 3e6/MW my_model <- function() { ini({    # Typical Value of System Parameters     TV_F1           = fixed(c(.Machine$double.eps, 0.744, .Machine$double.xmax))    TV_ka           =       c(.Machine$double.eps, 0.282, .Machine$double.xmax)    TV_CL           =   log(c(.Machine$double.eps, 0.200, .Machine$double.xmax))    TV_Vc           =   log(c(.Machine$double.eps, 3.61, .Machine$double.xmax))    TV_Vp           =   log(c(.Machine$double.eps, 2.75, .Machine$double.xmax))    TV_Q            =   log(c(.Machine$double.eps, 0.747, .Machine$double.xmax))    TV_MW           = fixed(c(.Machine$double.eps, 140, .Machine$double.xmax))    TV_BM_IC        =       c(.Machine$double.eps, 1000, .Machine$double.xmax)    TV_kdeg_BM      = fixed(c(.Machine$double.eps, 0.1, .Machine$double.xmax))    TV_Emax         =       c(.Machine$double.eps, 100.0, .Machine$double.xmax)    TV_EC50         = fixed(c(.Machine$double.eps, 300, .Machine$double.xmax))     # Between-subject variability:      ETAka +     ETACL +     ETAVc +     ETAVp +     ETAQ +      ETABM_IC +  ETAEmax ~    c(0.416,                                                                               0,          0.09875,                                                                  0,          0.0786,     0.116,                                                       0,          0.0619,     0.0377,     0.0789,                                          0,          0,          0,          0,          0.699,                               0,          0,          0,          0,          0,          0.05,                    0,          0,          0,          0,          0,          0,          0.10)       # Error model parameters    prop_err_PK  =  c(.Machine$double.eps, 0.1, .Machine$double.xmax)    add_err_PK   =  c(.Machine$double.eps, 0.1, .Machine$double.xmax)    add_err_BM   =  c(.Machine$double.eps, 0.1, .Machine$double.xmax)  }) model({     # System Parameters     F1          = TV_F1    ka          = TV_ka*exp(ETAka)    CL          = exp(TV_CL)*exp(ETACL)    Vc          = exp(TV_Vc)*exp(ETAVc)    # AMTIFY:Cc    Cc          = Ac/(Vc)    Vp          = exp(TV_Vp)*exp(ETAVp)    Q           = exp(TV_Q)*exp(ETAQ)    MW          = TV_MW    BM_IC       = TV_BM_IC*exp(ETABM_IC)    kdeg_BM     = TV_kdeg_BM    Emax        = TV_Emax*exp(ETAEmax)    EC50        = TV_EC50     Dinf        = 0.0     # Static Secondary Parameters     WTTV        = 70     CL_IND      = CL*(1.0+SEX_ID*.1)*(1.0+SUBTYPE_ID*.1)     kel         = CL_IND/Vc*((WT/WTTV))^(-0.35)     kcp         = Q/Vc*((WT/WTTV))^(-0.35)     kpc         = Q/Vp*((WT/WTTV))^(-0.35)     ksyn_BM     = kdeg_BM*BM_IC      # Dynamic Secondary Parameters     STIM        = 1.0+ Emax*Cc/(EC50+Cc)      # Defining ODEs    BM(0)       = BM_IC    d/dt(At)    = (-ka*At)    d/dt(Ac)    = ((ka*At*F1/Vc  -kel*Cc - kcp*Cc  + kpc*Cp*Vp/Vc + Dinf/Vc))*Vc    d/dt(Cp)    = (kcp*Cc*Vc/Vp - kpc*Cp)    d/dt(BM)    = ksyn_BM*STIM-(kdeg_BM*BM)     # Outputs and error models    C_ng_ml     = Cc*MW    C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)     BM_obs      = BM    BM_obs  ~ add(add_err_BM)  }) }"},{"path":"/articles/deployment.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Deployment and Customization","text":"Running ruminate simple straight forward using ruminate function: default presents modules standard format. However may wish deploy ruminate internal server customize .","code":"library(ruminate) ruminate()"},{"path":"/articles/deployment.html","id":"deployment","dir":"Articles","previous_headings":"","what":"Deployment","title":"Deployment and Customization","text":"deploy ruminate can create copy app based template package: Certain features app behave differently depending whether running locally deployed server. tell App deployed need create empty file named DEPLOYED directory deployed app file. two files (App.R DEPLOYED) placed direcory shiny server ruminate installed enough deploy basic app.","code":"file.copy(from = system.file(package=\"ruminate\",\"templates\", \"ruminate.R\"),            to   = \"App.R\") file.create(\"DEPLOYED\")"},{"path":"/articles/deployment.html","id":"customization","dir":"Articles","previous_headings":"","what":"Customization","title":"Deployment and Customization","text":"can open App.R file run app locally well. can also edit file customize app. includes default text presented, graphics might want, also color scheme. Beyond individual modules also customizable.","code":""},{"path":"/articles/deployment.html","id":"app-layout","dir":"Articles","previous_headings":"Customization","what":"App Layout","title":"Deployment and Customization","text":"app built around modules overall look introductory text controlled file. default app uses shinydashboard package. can look documentation package alter tabs, default skin color, module icons, etc. default package contains modules, can simply remove tabs modules want.","code":""},{"path":"/articles/deployment.html","id":"individual-modules","dir":"Articles","previous_headings":"Customization","what":"Individual Modules","title":"Deployment and Customization","text":"top App.R file locations module configuration files defined: controls different aspects module including text displayed, icons used, certain behaviors app, default values, etc. options described comments configuration files. use need first make copy specific configuration file. example, customize NCA module make copy NCA.yaml file: can make changes want myNCA.yaml file. need specify App.R file want use new configuration file. can thing modules well.","code":"formods.yaml  = system.file(package=\"formods\",  \"templates\",  \"formods.yaml\") ASM.yaml      = system.file(package=\"formods\",  \"templates\",  \"ASM.yaml\") UD.yaml       = system.file(package=\"formods\",  \"templates\",  \"UD.yaml\") DW.yaml       = system.file(package=\"formods\",  \"templates\",  \"DW.yaml\") FG.yaml       = system.file(package=\"formods\",  \"templates\",  \"FG.yaml\") NCA.yaml      = system.file(package=\"ruminate\", \"templates\",  \"NCA.yaml\") file.copy(from = system.file(package=\"ruminate\", \"templates\",  \"NCA.yaml\"),           to   = \"myNCA.yaml\") NCA.yaml      = \"myNCA.yaml\""},{"path":"/articles/model_builder.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Model Building","text":"model building module constructed around rxode2 package. allows load base model either model catalog user-create file. loaded user can modify model (change structure, parameter values, etc), make copy, etc. can upload model rxode2 format also NONMEM format. NONMEM file (control stream listing output) converted upload rxode2 using nonmem2rx package.","code":""},{"path":"/articles/model_builder.html","id":"model-catalog","dir":"Articles","previous_headings":"","what":"Model catalog","title":"Model Building","text":"model catalog built sources field configuration file. can include models nlmixr2lib model library. can also include user defined files. specified found, skipped. Edit MB yaml file alter .","code":""},{"path":"/articles/model_builder.html","id":"user-defined-models","dir":"Articles","previous_headings":"Model catalog","what":"User-defined models","title":"Model Building","text":"User-defined models can included catalog uploaded app. models can specified either NONMEM rxode2 formats. NONMEM models can control stream (good), listing output estimation (better), XML output estimation (best). use nlmixr2 model simply need function definition assigned object my_fcn. examples see: system.file(package=\"ruminate\", \"templates\", \"MB_model_rxode2.R\") system.file(package=\"ruminate\", \"templates\", \"MB_model_NONMEM.ctl\") Modules specified sources section configuration file. default configuration file look working directory App user_model.R user_model.ctl. rxode2 model can name user_model.R place app directory included catalog. Similarly NONMEM control stream can name user_model.ctl, included automatically. exist skipped. can create many entries want.","code":""},{"path":"/articles/model_builder.html","id":"configuration-file","dir":"Articles","previous_headings":"","what":"Configuration File","title":"Model Building","text":"","code":"# The MC field is a skeleton for constructing new modules MC:   # The prefix of object names created in the generated code   element_object_name: \"MB_obj\"   # This is a list of model sources. In the app the sources will be listed in   # the order shown here. This is also referred to as the model catalog. If   # the type is set to nlmixr2lib it will insert the models from nlmixr2lib.   # User defined models can be used as well. See the examples below for   # rxode2 and NONMEM models.   #   # - group: This is how models will be grouped in the selection pulldown.   # - name: Short name for the model: **NOTE that names need to be unique**.   # - description: Verbose description of the model.   # - type: Supported model types are \"nlmixr2lib\", \"rxode2\" or \"NONMEM\".   # - obj: For the rxode2 type you need to also specify the name of the object   #   that is created. This option is only used for the rxode2 type.   # - file: For both rxode2 and nlmixr2 you need to specify a character string   #   with a file path. This will be evaluated as an R command, so keep that   #   in mind. If the file is not found it will be skipped when the app loads.   sources:   - source:       group:       \"User-defined Models\"       name:        \"rxode2 User model\"       description: \"User-defined rxode2 model\"       type:        \"rxode2\"       obj:         \"my_fcn\"       file:        'file.path(getwd(),\"user_model.R\")'   - source:       group:       \"User-defined Models\"       name:        \"NONMEM User model\"       description: \"User-defined NONMEM model\"       type:        \"NONMEM\"       file:        'file.path(getwd(),\"user_model.ctl\")'   - source:       group:       \"Model Library\"       type:        \"nlmixr2lib\"       name:        \"nlmixr2 Model Library\" # The include section is optional. You can use this section to copy files # from the directory where you are running the app to the user directory to be # used in the app and when saved. The source and dest options can be a path # command like below or a double # quoted string: '\"/path/to/file\"' # The dest should be relative to user directory (think the root of the zip # file that is exported). See formods.yaml for examples. # # include: #   files: #   - file: #       source: 'file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\")' #       dest:   'file.path(\"config\",\"report.docx\")'   code: # module specific code options     packages: [ \"rxode2\", \"nonmem2rx\", \"nlmixr2lib\"]     readOnly: TRUE     mode:     r   # Controls what is shown in the compact ui mode:   compact:     code:    TRUE     clip:    TRUE   # This is where you put reporting options. The expected options are shown   # here, but module-specific options can also be added if needed:   reporting:     enabled: FALSE     # Priority of reporting relative to other modules     # (higher numbers are reported first)     priority: 1   # put different formatting options (widths, heights,   # shinyWidgets button sizes, etc) here.   formatting:     # Dimensions of code snippet box     code:       width:  800       height: 300     preview:       width:  \"800px\"       height: \"500px\"     input:     current_element:       width:  \"200px\"       tooltip: \"Change the current model.\"       tooltip_position: \"right\"     element_name:       width:  \"200px\"     button_clk_run:       size: \"sm\"       block: TRUE     button_clk_del:       size: \"sm\"       block: TRUE       tooltip: \"Delete the current model.\"       tooltip_position: \"right\"     button_clk_save:       size: \"sm\"       block: TRUE       tooltip: \"Save the name for the current model.\"       tooltip_position: \"right\"     button_clk_clip:       size: \"sm\"       block: TRUE       tooltip: \"Copy the code to generate the current model to the clipboard.\"       tooltip_position: \"right\"     button_clk_copy:       size: \"sm\"       block: TRUE       tooltip: \"Make a copy of the current model.\"       tooltip_position: \"right\"     button_clk_new:       size: \"sm\"       block: TRUE       tooltip: \"Create a new model.\"       tooltip_position: \"right\"       width: 100     catalog_selection:       width:   \"200px\"       truncate: 65       tooltip:  \"Select base model from the catalog.\"       tooltip_position: \"top\"     model_type_selection:       width: \"200px\"       tooltip: \"Select the type of the model being uploaded.\"       tooltip_position: \"right\"       choices:         rxode2: \"rxode2 function\"         NONMEM: \"NONMEM (XML, lst, or ctl)\"       default:   \"rxode2\"     upload_model_file:       width: \"400px\"     base_from:       size:    \"normal\"       status:  \"primary\"       tooltip: \"You can select the base model from a catalog or upload your own\"       tooltip_position: \"bottom\"       choices:         catalog: \"Model Catalog\"         user:    \"User-defined model\"       default: \"catalog\"     # The timescales here can be altered manually. This is the general format:     #   short_name:     #     conv: \"10\"     #     verb: \"A more verbose name\"     # Generally you dont want to  change what is already here in terms of     # these short name used. You can remove timescales you don't want to use     # or add timescales that you need but are not present.     #   -verb  You can change the verb field (what the user sees).     # The order of Appearance is the same order seen here. The default     # is the selected short name for the specified timescale     time_scales:       width: \"200px\"       default: \"weeks\"       tooltip: \"Choose the timescale of the model.\"       tooltip_position: \"top\"       choices:         months:           conv: \"60*60*24*4\"           verb: \"Months\"         weeks:           conv: \"60*60*24*7\"           verb: \"Weeks\"         days:           conv: \"60*60*24\"           verb: \"Days\"         hours:           conv: \"60*60\"           verb: \"Hours\"         min:           conv: \"60\"           verb: \"Minutes\"         sec:           conv: \"1\"           verb: \"Seconds\"   labels:     current_element: NULL # \"Select model\"     element_name :   NULL # \"model name\"     upload_model_file:    NULL     base_from:            NULL     catalog_selection:    NULL     model_type_selection: NULL     time_scale:           NULL     catalog_empty:        \"No models were found, the catalog is empty\"     save_btn:             \"Save\"     clip_btn:             \"Code\"     copy_btn:             \"Copy\"     del_btn:              \"Delete\"     new_btn:              \"New\"     building_model:       \"Building model, be patient.\"     element_name_diff:    \"The model name has changed.\"     model_code_diff:      \"Manual changes have been made to the model.\"     save_change_detected: \"You need to click on the save button for these changes to take effect.\"     head_base_model:      \"Starting model\"     head_model_code:      \"Model code\"     head_time_scale:      \"Model time-scale\"   errors:     no_model_found:             \"No model was found.\"     base_model_build_failed:    \"The base model build failed.\"     manual_model_update_failed: \"Manual model update failed.\"     user_file_upload_failed:    \"User-defined model failed.\"     nlmixr2lib_not_found:       \"The nlmixr2lib package was not found. This library will not be available.\"     selected_id_bad_list:       \"Unable to find a list for the selected component.\"     selected_id_bad_row:        \"Selected component should have 1 row.\"   tooltips:     # Set to FALSE to disable tool tips for this module     include: TRUE     elements: \"Show model components\"     show_code: \"Show model code\"     url_rxode: \"https://nlmixr2.github.io/rxode2/articles/rxode2-syntax.html\"     url_model_types: \"https://ruminate.ubiquity.tools/articles/model_builder.html#model-catalog\"     components:       input_tip: \"My tool tip\""},{"path":"/articles/noncompartmental_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Noncompartmental Analysis","text":"Non-compartmental analysis (NCA) simple quick method evaluating exposure drug. allows evaluate things like linearity vivo exposure. illustrate consider antibody given subcutaneous injection. actual exposure profile patient might experience given solid black line left panel. don’t yet ability sample continuous fashion. might observer given blue points.  Generally NCA determine following directly data: Cmax - Maximum observed concentration (units=concentration) Tmax - time maximum concentration observed (units=time) AUC - area curve (\\(units=time \\times concentration\\)) AUMC - area first moment curve (\\(units=time^2 \\times concentration\\)) properties based observational data. Cmax Tmax certainly actual maximum concentration long sample judiciously give us good approximation. Similarly, calculated AUC AUMC different actual values. calculate areas need dig back calculus text books trapezoid method. Basically sampling interval trapezoid area calculated added n samples: \\[ \\begin{eqnarray} AUC  &=& \\int_0^{t_f} Cdt &\\approx  \\sum_{=1}^{n-1}{\\frac{C_i+C_{+1}}{2}\\times (t_{+1}-t_{})}  \\\\ AUMC &=& \\int_0^{t_f} t\\times Cdt &\\approx  \\sum_{=1}^{n-1}{\\frac{t_iC_i+t_{+1}C_{+1}}{2}\\times (t_{+1}-t_{})} \\end{eqnarray} \\] can done Excel pretty easily. Depending data analysis properties can calculated. example can calculate clearance, mean residence time, steady-state volume distribution terminal half-life: Clearance: \\(CL = \\frac{Dose}{AUC}\\) Mean residence time: \\(MRT = \\frac{AUMC}{AUC}\\) Steady state volume distribution: \\(V_{ss} = MRT \\times CL\\) Half-life: Terminal slope natural log data Properties like AUC AUMC can also calculated using extrapolation last time point infinity account data beyond observations hand. subsequent values clearance, volumes distribution, etc can also change extrapolation. lot nuance associated calculations, good rely software focuses type analysis. PKNCA package developed mind. ruminate app provides interface functionality found PKNCA generate code can use get started PKNCA.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"data-format","dir":"Articles","previous_headings":"","what":"Data format","title":"Noncompartmental Analysis","text":"run NCA need data formatted specific way. names columns can defined user. columns required, others optional, others required depending user wants specify dosing. See general description columns followed information dosing inferred: Subject ID: Unique id subject dataset (required) Time: Observation time since first dose (required) Nominal Time: Time since recent dose (optional, needed determine dosing columns) Dose: Amount dose (required) Concentration: Observed concentration (NA missing values, required) Route: Dosing route (intravascular extravascular, required) Grouping: columns used grouping (optional) Dose Duration: Duration infusion (optional) Dosing Cycle Period: recent dose cycle period (optional, needed dosing columns) Event ID: EVID column NONMEM used distinguish dosing observation records (optional, needed dosing rows) Analyte: Used multiple analytes present dataset (optional) Note Dose Concentration need mass units.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"dosing-information","dir":"Articles","previous_headings":"Data format","what":"Dosing information","title":"Noncompartmental Analysis","text":"Dosing extracted datasets, can incorporated two ways. One dosing information stored columns dataset. simplest straight forward way storing dosing information. method dosing specified individual records (rows) dataset (data formatted NONMEM, can use ).","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"determining-dosing-from-columns","dir":"Articles","previous_headings":"Data format > Dosing information","what":"Determining dosing from columns","title":"Noncompartmental Analysis","text":"Beyond required columns infer dosing columns must also following columns defined: Dose, Dosing Cycle Period, Nominal Time.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"determining-dosing-from-rows-nonmem-format","dir":"Articles","previous_headings":"Data format > Dosing information","what":"Determining dosing from rows (NONMEM format)","title":"Noncompartmental Analysis","text":"dataset formatted NONMEM can extract dosing rows contain dosing records. case expected following columns dataset: Dose (AMT NONMEM) Event ID (EVID NONMEM). notes dosing information. interval want calculate dose-dependent parameters, need provide information dosing intervals. multiple dose setting intervals enough PK data run NCA , necessary provide dosing information intervals. example four weekly doses intensive sampling weeks 1 4 peak trough weeks 2 3, provide dosing information weeks 2 3. However certain parameters PKNCA provide dosing intermediate sampling information provided (e.g. time steady-state). interested parameters intervals intensive sampling, don’t need worry dosing information intervals.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"nca-analysis-intervals","dir":"Articles","previous_headings":"Data format","what":"NCA analysis intervals","title":"Noncompartmental Analysis","text":"select interval analysis, necessary observations beginning end interval. observations dosing times (e.g. dose time zero first observation 15 minutes post-dose) want use dosing time beginning interval, can observation BQL missing (NA) dosing time (zero).","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"nca-parameters","dir":"Articles","previous_headings":"Data format","what":"NCA parameters","title":"Noncompartmental Analysis","text":"following shows different NCA parameters can calculated. PKNCA parameter value used PKNCA, App Parameter Name value shown app.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"configuration-file","dir":"Articles","previous_headings":"","what":"Configuration File","title":"Noncompartmental Analysis","text":"","code":"MC:   code: # module specific code options     packages: [\"PKNCA\", \"dplyr\", \"stringr\", \"formods\", \"ruminate\", \"onbrand\"]     readOnly: TRUE     mode:     r     preamble: |-       # Non-compartmental analysis ----------------------------------------------   compact:     code:    TRUE     clip:    TRUE   reporting:     enabled: TRUE     # Text format determines how text will be rendered. It can be either \"md\"     # for Markdown or \"text\" for plain text. This will also control things     # like table headers.     text_format: \"md\"     # Priority of reporting relative to other modules (higher numbers are     # reported first)     priority: 10     # The order of the figure and table ids (e.g. fg_ind_obs) represents the     # order in which figures and tables will be reported. Each id should have     # the following optoins:     #     #   caption_single      Caption used when a table or figure needs only a     #                       single page.     #     #   caption_multiple    Caption used when a table or figure spans multiple     #                       pages.     #     #   title_single        Slide title used when a table or figure needs only a     #                       single page.     #     #   title_multiple      Slide title used when a table or figure spans multiple     #                       pages.     #     #   sub_title_single    Slide sub_title used when a table or figure needs only a     #                       single page.     #     #   sub_title_multiple  Slide sub_title used when a table or figure spans multiple     #                       pages.     #     # The following placeholders can be used bu surroudning them with ===. For     # example to include the total number of figures you would use     # ===FIGTOT===:     #     # NCADESC               Description of the NCA analysis provided by the     #                       user in the UI (tables and figures).     #     # FIGNUM                Number of the current figure for figures that span     #                       multiple pages (figures only).     #     # FIGTOT                Total number of figures for figures that span     #                       multiple pages (figures only).     #     # TABNUM                Number of the current table for tables that span     #                       multiple pages (tables only).     #     # TABTOT                Total number of tables for tables that span multiple     #                       pages (tables only).     #     #     figures:       fg_ind_obs:         caption_single:     \"===NCADESC=== (Individual Observations)\"         caption_multiple:   \"===NCADESC=== (Individual Observations,  ===FIGNUM=== of ===FIGTOT===)\"         title_single:       \" Individual Observations\"         title_multiple:     \" Individual Observations\"         sub_title_single:   \"===NCADESC===\"         sub_title_multiple: \"===NCADESC=== (===FIGNUM=== of ===FIGTOT===)\"     tables:       tb_ind_params:         caption_single:   \"===NCADESC=== (Individual Parameters)\"         caption_multiple: \"===NCADESC=== (Individual Parameters, ===TABNUM=== of ===TABTOT===)\"       tb_ind_obs:         caption_single:   \"===NCADESC===, Individual Data\"         caption_multiple: \"===NCADESC===, Individual Data (===TABNUM=== of ===TABTOT===)\"     # Default number of significant digits     digits: 3     # When reporting units the * will be replaced with this value:     mult_str: \"⋅\"     # You can use unicode here like this \"∞\"     # How infinity is represented in reports like table headers     # to change inf in parameters you need to change them for the individual     # parameters below.     infinity:    \"inf\"     not_calc:    \"NC\"     not_sampled: \"NS\"     blq:         \"BLQ\"     # Here we list all the notes that can be present in tables. You need an     # entry for whatever you put in not_calc and blq above here.     notes:       NC: \"not calculated\"       NS: \"not sampled\"       BLQ: \"below the level of quantification\"   notifications:     ana_add_int_success: \"Interval added: ===DETAILS===\"   # Put different formatting options (widths, heights,   # shinyWidgets button sizes, etc) here.   formatting:     # Dimensions of code snippet box     code:       width:  800       height: 300     # Dimensions of the notes text area     notes:       width:  \"520px\"       height: \"75px\"     # Width of textInput and selectInput UI elements in the     # nca configuration     nca_config_option:       width: 150     # Width of the figure preview (must be numeric)     # The options nrow and ncol refer to the default number of rows and     # columns when faceting. The options nrow_opt and nrow_col are values the     # user can select.     #     preview:       width:           \"100%\"       height:          \"600px\"       nrow:            4       ncol:            4       nrow_opt:        [ 1, 2, 3, 4, 5, 6, 7, 8 ]       ncol_opt:        [ 1, 2, 3, 4, 5, 6, 7, 8 ]       logy:            TRUE       fig_type:        \"report\"       tab_type:        \"report\"     # These apply to the intervals table     dose_from:       default:          \"cols\"       tooltip:          \"Dosing information can be found in either columns (column for dose, dose cycle, nominal time) or rows (one row for each dose event distinguished by an event ID (EVID) column).\"       tooltip_position: \"top\"     intervals:       width:  600       height: 175     intervals_start:       width:  50     intervals_stop:       width:  50     intervals_np_text:       width:  375     intervals_delete:       width:  75     button_ana_run:       size: \"sm\"       block: TRUE     button_ana_del:       size: \"sm\"       block: TRUE       tooltip: \"Delete the current analysis.\"       tooltip_position: \"right\"     button_ana_save:       size: \"sm\"       block: TRUE       tooltip: \"Save the caption and notes and apply the data view for the current analysis.\"       tooltip_position: \"right\"     button_ana_clip:       size: \"sm\"       block: TRUE       tooltip: \"Copy the code to generate the current analysis to the clipboard.\"       tooltip_position: \"right\"     button_fg_save:       size: \"sm\"       block: TRUE      #tooltip: \"tooltip\"      #tooltip_position: \"right\"     button_tb_save:       size: \"sm\"       block: TRUE      #tooltip: \"tooltip\"      #tooltip_position: \"right\"     button_ana_copy:       size: \"sm\"       block: TRUE       tooltip: \"Make a copy of the current analysis.\"       tooltip_position: \"right\"     button_ana_new:       size: \"sm\"       block: TRUE       tooltip: \"Create a new analysis.\"       tooltip_position: \"right\"     button_ana_add_int:       size: \"sm\"       block: TRUE     button_ana_use_scenario:       size: \"sm\"       block: TRUE     check_fg_ind_obs:       width: 75     select_ana_interval_range:       width:  200     text_ana_interval_start:       width: 75     text_ana_interval_stop:       width: 75     select_current_view:       width: 200     select_ana_fig_view:       width: 250     select_ana_tab_view:       width: 250     select_ana_source_sampling:       width: 150     select_ana_nca_parameters:       width: 150     select_ana_col_id:       width: 150       tooltip:  \"Unique id for each subject in the dataset (required)\"       tooltip_position: \"bottom\"     select_ana_col_dose:       width: 150       tooltip: \"Amount of dose (required)\"       tooltip_position: \"bottom\"     select_ana_col_dur:       width: 150       tooltip: \"Duration of infusion (optional)\"       tooltip_position: \"bottom\"     select_ana_col_conc:       width: 150       tooltip: \"Observed concentration (NA for missing values, required)\"       tooltip_position: \"bottom\"     select_ana_col_route:       width: 150       tooltip: \"Dosing route (intravascular  or extravascular, required)\"       tooltip_position: \"bottom\"     select_ana_col_time:       width: 150       tooltip: \"Observation time since the first dose (required)\"       tooltip_position: \"bottom\"     select_ana_col_ntime:       width: 150       tooltip: \"Time since the most recent dose (optional, needed to determine dosing from columns)\"       tooltip_position: \"bottom\"     select_ana_col_group:       width: 150       tooltip: \"Other columns used for grouping (optional)\"       tooltip_position: \"bottom\"     select_ana_col_analyte:       width: 150       tooltip: \"Used if multiple analytes are present in the dataset (optional)\"       tooltip_position: \"bottom\"     select_ana_col_evid:       width: 150       tooltip: \"EVID column from NONMEM used to distinguish dosing and observation records (optional, needed for dosing from rows)\"       tooltip_position: \"bottom\"     select_ana_col_cycle:       width: 150       tooltip: \"The most recent dose cycle or period (optional, needed for dosing from columns)\"       tooltip_position: \"bottom\"     select_ana_units_dose:       width: 150     select_ana_units_time:       width: 150     select_ana_units_conc:       width: 150     select_ana_units_amt:       width: 150     select_ana_scenario:       width: 150     select_fg_ind_obs_ncol:       width: 50     select_fg_ind_obs_nrow:       width: 50     select_fg_ind_obs_page:       width: 100     select_tb_page:       width: 100   errors:     nca_run_filed: \"Unable to run PKNCA. Please, see below for details.\"     bad_input: \"Input error message\"     nca_no_fig: \"There are no figures to display. You must run the analysis first\"     nca_no_tab: \"There are no tables to display. You must run the analysis first\"   tooltips:     # Set to FALSE to disable tool tips for this module     include: TRUE     notes:             \"Optional analysis notes that will be carried over and used in other elements (e.g. reporting).\"     ana_key:           \"Unique description used for referencing analysis and in other elements (e.g. reporting).\"     show_code:         \"Show analysis code\"     nca_intervals:     \"Current NCA intervals\"     # Set urls to NULL to disable     url_dosing:        \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#dosing-information\"     url_data:          \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#data-format\"     url_intervals:     \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#nca-analysis-intervals\"     url_parameters:    \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#nca-parameters\"     ph:       text_ana_interval_start: 0       text_ana_interval_stop:  \"number or Inf\"       notes:                   \"Optional NCA notes that will be carried over and used in other elements (e.g. reporting).\"   # This controls the figure and table selection for an analysis. The order of   # the keys (e.g., tb_ind_params, tb_sum_params, etc) controls the   # order they are dispalyed in the selector and the text controls what's show   # in the interface.   figures:     fg_ind_obs:       choice:  \"Individual Profiles\"       subtext: \"Faceted view of individual time-course\"   tables:     tb_ind_params:       choice:  \"Individual NCA Parameters\"       subtext: \"Listings of subject-level NCA parmaeters\"     tb_sum_params:       choice:  \"Summarized NCA Parameters\"       subtext: \"Listings of NCA parmaeters summarized by group/analyte\"     tb_ind_obs:       choice:  \"Individual Data\"       subtext: \"Listings of subject-level time-course data\"     tb_nca_raw:       choice:  \"Raw NCA Output\"       subtext: \"Output from PKNCA with requested and dependent parameters\"   labels:     ana_key:                         \"title/caption\"     new_ana:                         \"New\"     run_ana:                         \"Run NCA\"     save_ana:                        \"Save\"     del_ana:                         \"Delete\"     copy_ana:                        \"Copy\"     clip_ana:                        \"Code\"     fg_save:                         \"Apply changes\"     tb_save:                         \"Apply changes\"     curr_anas_none:                  \"No figures yet created\"     no_dataset:                      \"No datasets available to analyze. You need to load a dataset to use this module.\"     ana_use_scenario:                \"Analysis Template\"     ana_add_int:                     \"Add/Update Interval\"     no_intervals:                    \"No intervals added yet\"     panel_analysis_opts:             \"Analysis Details\"     panel_figures:                   \"Figures\"     panel_tables:                    \"Tables\"     panel_nca_config:                \"NCA Options\"     text_ana_interval_start:         \"Start\"     text_ana_interval_stop:          \"End\"     select_ana_interval_range:       \"Interval Range\"     # Headings used in the UI     head_intervals:                  \"Analysis Time Intervals\"     head_intervals_current:          \"Current Intervals\"     head_analysis_template:          \"Use Analysis Template\"     head_intervals_create:           \"Create an Interval\"     head_dose_from:                  \"Dosing Information\"     head_col_mapping:                \"Identify Columns\"     head_col_mapping_required:       \"Required\"     head_col_mapping_optional:       \"Optional\"     head_run_analysis:               \"Run Analysis\"     head_units:                      \"Units\"     select_current_view:             \"Data from\"     select_current_ana:              \"Current NCA\"     select_ana_scenario:             NULL  # \"Type of Analysis:\"     select_ana_col_id:               \"Subject ID\"     select_ana_col_time:             \"Time\"     select_ana_col_ntime:            \"Nominal Time\"     select_ana_col_dose:             \"Dose\"     select_ana_col_dur:              \"Dose Duration\"     select_ana_col_conc:             \"Concentration\"     select_ana_col_route:            \"Route\"     select_ana_col_group:            \"Other Grouping\"     select_ana_col_cycle:            \"Dosing Cycle or Period\"     select_ana_col_evid:             \"Event ID\"     select_ana_col_analyte:          \"Analyte\"     select_ana_units_time:           \"Time\"     select_ana_units_dose:           \"Dose\"     select_ana_units_conc:           \"Concentration\"     select_ana_units_amt:            \"Amount\"     select_ana_nca_parameters:       \"Parameters\"     select_fg_ind_obs_nrow:          \"Rows\"     select_fg_ind_obs_ncol:          \"Columns\"     select_fg_ind_obs_page:          \"Show Page\"     select_tb_page:                  \"Show Page\"     check_fg_ind_obs_logy:           \"Log 10 y-scale\"     switch_ana_include_units:        \"Include Units\"     switch_ana_fig:                  NULL     switch_ana_fig_interactive:      \"Interactive\"     switch_ana_fig_report:           \"Report Preview\"     switch_ana_dose_from:            \"Dosing Found In\"     switch_ana_dose_from_rows:       \"Rows\"     switch_ana_dose_from_cols:       \"Columns\"     switch_ana_tab:                  NULL     switch_ana_tab_interactive:      \"Interactive\"     switch_ana_tab_report:           \"Report Preview\"     select_ana_source_sampling:      \"Data Sampling\"     busy:       run_nca:                       \"Running NCA be patient.\"     PKNCA_raw:       ID:                            \"Subject ID\"       start:                         \"Initial time of the analysis interval.\"       end:                           \"Final time of the analysis interval.\"       PPTESTCD:                      \"Parameter name from PKNCA.\"       PPORRES:                       \"parameter value.\"       exclude:                       \"Reason for parameter exclusion (NA when parameter was calculated).\"       PPORRESU:                      \"Parameter units.\"   # For details on the PKNCA options see this vignette:   # https://billdenney.github.io/pknca/articles/v40-options-for-controlling-PKNCA.html   # This section contains information about different options, metadata about   # them, default values, etc. Using this, the options UI for an analysis is   # generated automatically. Each configuration option has the following:   #   # group        - This is how configuration options will be grouped in the app   # label        - Short textual description   # tooltip      - More detailed description of the option and what goes into it.   #                Set to NULL to not display a tool tip.   # type         - List of possible types. For example if you have \"numeric\" then   #                \"character\" the value will be evaluated as numeric, if this is   #                NA then it will be evaluated as character. Allowed types:   #                  - numeric   #                  - character   #                  - mixed (numeric and character)   #                  - logical (TRUE or FALSE)   # value        - Default value in the UI   # options      - If set to NULL a text input box will be created for this option,   #                if there are options then a selection box will be created.   # pknca_option - The actual option set with PKNCA   nca_config:     adj_r_squared_factor:       group:         General       # BILL I need a better label for this       label:         \"r² (Adj) factor\"       tooltip:       \"The adjusted r² for the calculation of λz has this factor times the number of data points added to it. It allows for more data points to be preferred in the calculation of half-life.\"       type:          \"numeric\"       value:         0.0001       options:       NULL       pknca_option:  adj.r.squared.factor     max_missing:       group:         General       label:         \"Max missing (frac)\"       tooltip:       \"The maximum fraction of the data that may be missing (‘NA’) to calculate summary statistics.\"       type:          \"numeric\"       value:         0.5       options:       NULL       pknca_option:  max.missing     auc_method:       group:         AUC       label:         Method       tooltip:       \"The method used to calculate the AUC and related statistics.\"       type:          \"character\"       value:         \"lin up/log down\"       options:       [\"lin up/log down\", \"linear\"]       pknca_option:  auc.method     conc_na:       group:         Missing and BLQ Values       label:         Missing values       tooltip:       \"Determines how missing (NA) values are generally handled. Can be either drop or a numeric value to use. \"       type:          \"mixed\"       value:         \"drop\"       options:       NULL       pknca_option:  conc.na     conc_blq_first:       group:         Missing and BLQ Values       label:         First value BLQ       tooltip:       \"Indicates what to do if the first observation is BLQ. It can either be drop, keep, or a numeric value to use.\"       type:          \"mixed\"       value:         \"keep\"       options:       NULL       pknca_option:  conc.blq$first     conc_blq_middle:       group:         Missing and BLQ Values       label:         Middle values BLQ       tooltip:       \"Indicates what to do if a value between the first and last observations is BLQ. It can either be drop, keep, or a numeric value to use.\"       type:          \"mixed\"       value:         \"drop\"       options:       NULL       pknca_option:  conc.blq$middle     conc_blq_last:       group:         \"Missing and BLQ Values\"       label:         \"Last Value BLQ\"       tooltip:       \"Indicates what to do if the last observation is BLQ. It can either be drop, keep, or a numeric value to use.\"       type:          \"mixed\"       value:         \"keep\"       options:       NULL       pknca_option:  conc.blq$last     first_tmax:       group:         \"General\"       label:         \"Multiple tmax use first\"       tooltip:       \"If there is more than one concentration equal to Cmax, should the time selected for Tmax be the first value? If TRUE, the first will be selected. If FALSE, the last will be selected.\"       type:          \"logical\"       value:         TRUE       options:       [TRUE, FALSE]       pknca_option:  first.tmax     allow_tmax_in_half_life:       group:         \"Half-Life\"       label:         \"Allow tmax in half-life\"       tooltip:       \"Should the concentration and time at Tmax be allowed in the half-life calculation? TRUE is yes and FALSE is no.\"       type:          \"logical\"       value:         FALSE       options:       [TRUE, FALSE]       pknca_option:  allow.tmax.in.half.life     min_hl_points:       group:         \"Half-Life\"       label:         \"Min points\"       tooltip:       \"What is the minimum number of points required to calculate half-life?\"       type:          \"numeric\"       value:         3       options:       [ 2, 3, 4, 5 ]       pknca_option:  min.hl.points     min_span_ratio:       group:         \"Half-Life\"       label:         \"Min span ratio\"       # BILL this is a bit cryptic       tooltip:       \"What is the minimum span ratio required to consider a half-life valid?\"       type:          \"logical\"       value:         2       options:       NULL       pknca_option:  min.span.ratio     min_hl_r_squared:       group:         \"Half-Life\"       label:         \"Min r² value\"       tooltip:       \"What is the minimum r² value to consider a half-life calculation valid?\"       type:          \"numeric\"       value:         0.9       options:       NULL       pknca_option:  min.hl.r.squared     max_aucinf_pext:       group:         \"AUC\"       label:         \"Max allowed extrap (%)\"       tooltip:       \"What is the maximum percent extrapolation to consider an AUC∞ valid?\"       type:          \"numeric\"       value:         20       options:       NULL       pknca_option:  max.aucinf.pext   # Prefix used to create NCA analysis objects in generated code   nca_object_name: \"myANA_\"   # The following patterns are used when detecting columns in your dataset   # automatically. You can create a multiple patterns. They will be processed   # in order from left to right. The first pattern to match will return that   # column. For example if you set id to:   #   # id:   [\"^(?i)id$\",    \"(?i)subject\"]   #   # It will match any column that is id. The (?i) is case insensitive. But it   # wont match IDs for example because it looks for something that starts with   # i (^) and ends with d ($).   #   # If that fails it will look for any column with the text 'subject' in it   # regardless of case. So it would match SUBJECT, subject, Subjects,   # subject id, etc.   #   # If you set it to NULL no attempt will be made to detect the column.   detect_col:     id:      [\"^(?i)id$\",    \"(?i)subject\",    \"(?i)animal\"]     time:    [\"^(?i)time\",   \"(?i)ntime\"  ]     ntime:   [\"^(?i)ntime\",  \"(?i)time\"   ]     conc:    [\"^(?i)conc\",   \"^(?i)dv$\",      \"^(?i)pcstresn$\", \"^(?i)lbstresn$\", \"^(?i)AVAL(N|NUM)?$\"]     dose:    [\"(?i)dose\",    \"^(?i)ecdose?$\", \"^(?i)exdose?$\"]     evid:    [\"(?i)evid\"]     route:   [\"(?i)route\"]     cycle:   [\"(?i)cycle\",   \"(?i)dose_num\", \"(?i)period\"]     group:   NULL     analyte: NULL     dur:     NULL   # Routes may be specified in the dataset in a more detailed or convenient   # format than what is needed for analysis. Here you can specify patterns   # that can be used to detect routes. For details on the regular expressions   # that can be used see the detect_col section above. In general you can use   # the format of:   #   #   myroute: [\"regex1\", \"regex2\"]   #   # This will assign the route to 'myroute' in the ROUTE column if it matches   # regex1 or regex2.   #   detect_route:     intravascular: [\"^(?i)iv$\"]     extravascular: [\"^(?i)sc$\", \"^(?i)oral\"]   # These are default analysis scenarios that are shown in the App. They will   # be shown in the order defined. Each scenario has the following attributes:   #   - description: Free form textual description shown to the user   #   - nca_parameters: List of nca parameters to be calculated   #   - sampling: Can be either \"serial\" or \"sparse\"   #   BILL: Get relevant scenarios from bill here   ana_scenarios:     sd_iv:       description: \"Single Dose (IV)\"       nca_parameters: [\"cmax\", \"auclast\", \"aucinf.obs\"]       sampling:   \"serial\"       intervals:         - row:             start:         0             stop:          Inf             nca_parameters: [\"aucinf.obs\"]         - row:             start:         0             stop:          21             nca_parameters: [\"cmax\", \"auclast\"]     md_iv:       description: \"Multiple Dose IV\"       nca_parameters: [\"cmax\", \"auclast\", \"aucinf.obs\"]       sampling:   \"serial\"       intervals:         - row:             start:         0             stop:          Inf             nca_parameters: [\"cmax\", \"auclast\",\"aucinf.obs\"]     sd_iv_sparse:       description: \"Single Dose (IV) Sparse Sampling\"       nca_parameters: [\"cmax\", \"sparse_auclast\"]       sampling:   \"sparse\"       intervals:         - row:             start:          0             stop:           Inf             nca_parameters: [\"cmax\", \"sparse_auclast\"]     md_iv_sparse:       description: \"Multiple Dose IV Sparse Sampling\"       nca_parameters: [\"cmax\", \"sparse_auclast\"]       sampling:   \"sparse\"       intervals:         - row:             start:          0             stop:           Inf             nca_parameters: [\"cmax\", \"sparse_auclast\"]   # This is the default shown when the app loads or a new analysis is created   ana_scenario_def: \"sd_iv\"   # Here are all of the available NCA parameters. Each field is the name of   # the parameter used in PKNCA. See the table in this vignette for the   # available parameters:   #   # https://cran.r-project.org/web/packages/PKNCA/vignettes/v03-selection-of-calculation-intervals.html   #   # Each of parameter has the following   #   #  - description: This is a verbose description of the parameter. If set to NULL the   #                 description from PKNCA will be used. Set it to other text to   #                 overwrite it. This description will also be used in exports as   #                 well.   #   #  - text:        Textual description to show the user. If set to NULL it   #                 will use the text form PKCNA.   #   #  - md:          Markdown formatted value of text.   #   #  - group:       This is how the paramters will be grouped in the UI that   #                 is generated.   #   # To limit the options the user sees you simply need to comment out the   # parameters you do not want in the App. Alterantively you can use the   # grouping below to have the most common parameters together at the top and   # the remaining in case they are needed.   #   # BILL can you go through the text and md elements below and make any   # changes you think make sense. For example is there a better way to label   # the parameter aucint.inf.obs.dose :).   #   nca_parameters:     aucall:       description: NULL       text:        NULL       md:          \"AUC~all~\"       group:       \"Common Parameters\"     adj.r.squared:       description: NULL       text:        \"r² (Adj)\"       md:          \"Adj. R^2^\"       group:       \"Other\"     ae:       description: NULL       text:        NULL       md:          \"AE\"       group:       \"Other\"     aucall.dn:       description: NULL       text:        NULL       md:          \"AUC~all,dn~\"       group:       \"Other\"     aucinf.obs:       description: NULL       text:        NULL       md:          \"AUC~inf,obs~\"       group:       \"Other\"     aucinf.obs.dn:       description: NULL       text:        NULL       md:          \"AUC~inf,obs,dn~\"       group:       \"Other\"     aucinf.pred.dn:       description: NULL       text:        NULL       md:          \"AUC~inf,pred,dn~\"       group:       \"Other\"     aucint.all:       description: NULL       text:        NULL       md:          \"AUC~int,all~\"       group:       \"Other\"     aucint.all.dose:       description: NULL       text:        NULL       md:          \"AUC~int,all,dose~\"       group:       \"Other\"     aucint.inf.obs:       description: NULL       text:        NULL       md:          \"AUC~int,inf,obs~\"       group:       \"Other\"     aucint.inf.obs.dose:       description: NULL       text:        NULL       md:          \"AUC~int,inf,obs,dose~\"       group:       \"Other\"     aucint.inf.pred:       description: NULL       text:        NULL       md:          \"AUC~int,inf,pred~\"       group:       \"Other\"     aucint.inf.pred.dose:       description: NULL       text:        NULL       md:          \"AUC~int,inf,pred,dose~\"       group:       \"Other\"     aucint.last:       description: NULL       text:        NULL       md:          \"AUC~int,last~\"       group:       \"Other\"     aucint.last.dose:       description: NULL       text:        NULL       md:          \"AUC~int,last,dose~\"       group:       \"Other\"     aucivall:       description: NULL       text:        NULL       md:          \"AUC~iv,all~\"       group:       \"Other\"     aucivinf.obs:       description: NULL       text:        NULL       md:          \"AUC~iv,inf,obs~\"       group:       \"Other\"     aucivinf.pred:       description: NULL       text:        NULL       md:          \"AUC~iv,inf,pred~\"       group:       \"Other\"     aucivint.all:       description: NULL       text:        NULL       md:          \"AUC~iv,int,all~\"       group:       \"Other\"     aucivint.last:       description: NULL       text:        NULL       md:          \"AUC~iv,int,last~\"       group:       \"Other\"     aucivlast:       description: NULL       text:        NULL       md:          \"AUC~iv,last~\"       group:       \"Other\"     aucivpbextall:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,all~\"       group:       \"Other\"     aucivpbextinf.obs:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,obs~\"       group:       \"Other\"     aucivpbextinf.pred:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,pred~\"       group:       \"Other\"     aucivpbextint.all:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,all~\"       group:       \"Other\"     aucivpbextint.last:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,last~\"       group:       \"Other\"     aucivpbextlast:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,last~\"       group:       \"Other\"     auclast:       description: NULL       text:        NULL       md:          \"AUC~last~\"       group:       \"Other\"     auclast.dn:       description: NULL       text:        NULL       md:          \"AUC~last,dn~\"       group:       \"Other\"     aucpext.obs:       description: NULL       text:        NULL       md:          \"AUC~pext,obs~\"       group:       \"Other\"     aucpext.pred:       description: NULL       text:        NULL       md:          \"AUC~pext,pred~\"       group:       \"Other\"     aumcall:       description: NULL       text:        NULL       md:          \"AUMC~all~\"       group:       \"Other\"     aumcall.dn:       description: NULL       text:        NULL       md:          \"AUMC~all,dn~\"       group:       \"Other\"     aumcinf.obs:       description: NULL       text:        NULL       md:          \"AUMC~inf,obs~\"       group:       \"Other\"     aumcinf.obs.dn:       description: NULL       text:        NULL       md:          \"AUMC~inf,obs,dn~\"       group:       \"Other\"     aumcinf.pred.dn:       description: NULL       text:        NULL       md:          \"AUMC~inf,pred,dn~\"       group:       \"Other\"     aumclast:       description: NULL       text:        NULL       md:          \"AUMC~last~\"       group:       \"Other\"     aumclast.dn:       description: NULL       text:        NULL       md:          \"AUMC~last,dn~\"       group:       \"Other\"     c0:       description: NULL       text:        NULL       md:          \"C~0~\"       group:       \"Common Parameters\"     cav:       description: NULL       text:        NULL       md:          \"C~ave~\"       group:       \"Other\"     cav.dn:       description: NULL       text:        NULL       md:          \"C~ave,dn~\"       group:       \"Other\"     ceoi:       description: NULL       text:        NULL       md:          \"C~EOI~\"       group:       \"Other\"     cl.all:       description: NULL       text:        NULL       md:          \"CL\"       group:       \"Common Parameters\"     cl.last:       description: NULL       text:        NULL       md:          \"CL~last~\"       group:       \"Common Parameters\"     cl.obs:       description: NULL       text:        NULL       md:          \"CL~obs~\"       group:       \"Other\"     cl.pred:       description: NULL       text:        NULL       md:          \"CL~pred~\"       group:       \"Other\"     clast.obs:       description: NULL       text:        NULL       md:          \"CL~last,obs~\"       group:       \"Other\"     clast.pred:       description: NULL       text:        NULL       md:          \"CL~last,pred~\"       group:       \"Other\"     clast.pred.dn:       description: NULL       text:        NULL       md:          \"CL~last,pred,dn~\"       group:       \"Other\"     clr.last:       description: NULL       text:        NULL       md:          \"CL~R,last~\"       group:       \"Other\"     clr.obs:       description: NULL       text:        NULL       md:          \"CL~R,obs~\"       group:       \"Other\"     clr.pred:       description: NULL       text:        NULL       md:          \"CL~R,pred~\"       group:       \"Other\"     cmax:       description: NULL       text:        NULL       md:          \"C~max~\"       group:       \"Common Parameters\"     cmax.dn:       description: NULL       text:        NULL       md:          \"C~max,dn~\"       group:       \"Other\"     cmin:       description: NULL       text:        NULL       md:          \"C~min~\"       group:       \"Other\"     cmin.dn:       description: NULL       text:        NULL       md:          \"C~min,dn~\"       group:       \"Other\"     ctrough:       description: NULL       text:        NULL       md:          \"C~tr~\"       group:       \"Other\"     ctrough.dn:       description: NULL       text:        NULL       md:          \"C~tr,dn~\"       group:       \"Other\"     deg.fluc:       description: NULL       text:        NULL       md:          \"dgf\"       group:       \"Other\"     end:       description: NULL       text:        NULL       md:          \"t~end~\"       group:       \"Other\"     f:       description: NULL       text:        NULL       md:          \"f~b~\"       group:       \"Other\"     fe:       description: NULL       text:        NULL       md:          \"f~e~\"       group:       \"Other\"     half.life:       description: NULL       text:        NULL       md:          \"t~half~\"       group:       \"Common Parameters\"     kel.iv.last:       description: NULL       text:        NULL       md:          \"k~el,iv,last~\"       group:       \"Other\"     kel.iv.obs:       description: NULL       text:        NULL       md:          \"k~el,iv,obs~\"       group:       \"Other\"     kel.iv.pred:       description: NULL       text:        NULL       md:          \"k~el,iv,pred~\"       group:       \"Other\"     kel.last:       description: NULL       text:        NULL       md:          \"k~el,last~\"       group:       \"Other\"     kel.obs:       description: NULL       text:        NULL       md:          \"k~el,obs~\"       group:       \"Other\"     kel.pred:       description: NULL       text:        NULL       md:          \"k~el,pred~\"       group:       \"Other\"     lambda.z:       description: NULL       text:        \"λz\"       md:          \"<ff:symbol>l<\/ff>~z~\"       group:       \"Other\"     lambda.z.n.points:       description: NULL       text:        \"λz,n\"       md:          \"<ff:symbol>l<\/ff>~z,n~\"       group:       \"Other\"     lambda.z.time.first:       description: NULL       text:        \"λz (t 1ˢᵗ)\"       md:          \"<ff:symbol>l<\/ff>~z~ (t 1^st^)\"       group:       \"Other\"     mrt.iv.last:       description: NULL       text:        NULL       md:          \"MRT~iv,last~\"       group:       \"Other\"     mrt.iv.obs:       description: NULL       text:        NULL       md:          \"MRT~iv,obs~\"       group:       \"Other\"     mrt.iv.pred:       description: NULL       text:        NULL       md:          \"MRT~iv,pred~\"       group:       \"Other\"     mrt.last:       description: NULL       text:        NULL       md:          \"MRT~last~\"       group:       \"Other\"     mrt.md.obs:       description: NULL       text:        NULL       md:          \"MRT~md,obs~\"       group:       \"Other\"     mrt.md.pred:       description: NULL       text:        NULL       md:          \"MRT~md,pred~\"       group:       \"Other\"     mrt.obs:       description: NULL       text:        NULL       md:          \"MRT~obs~\"       group:       \"Other\"     mrt.pred:       description: NULL       text:        NULL       md:          \"MRT~pred~\"       group:       \"Other\"     ptr:       description: NULL       text:        NULL       md:          \"PTR\"       group:       \"Other\"     r.squared:       description: NULL       text:        \"r²\"       md:          \"R^2^\"       group:       \"Common Parameters\"     span.ratio:       description: NULL       text:        NULL       md:          \"SR\"       group:       \"Other\"     sparse_auclast:       description: NULL       text:        NULL       md:          \"AUC~last,sp~\"       group:       \"Common Parameters\"     start:       description: NULL       text:        NULL       md:          \"t~start~\"       group:       \"Other\"     swing:       description: NULL       text:        NULL       md:          \"SWING\"       group:       \"Other\"     tfirst:       description: NULL       text:        \"t 1ˢᵗ\"       md:          \"t~first~\"       group:       \"Other\"     thalf.eff.iv.last:       description: NULL       text:        NULL       md:          \"t~half,eff,iv,last~\"       group:       \"Other\"     thalf.eff.iv.obs:       description: NULL       text:        NULL       md:          \"t~half,eff,iv,obs~\"       group:       \"Other\"     thalf.eff.iv.pred:       description: NULL       text:        NULL       md:          \"t~half,eff,iv,pred~\"       group:       \"Other\"     thalf.eff.last:       description: NULL       text:        NULL       md:          \"t~half,eff,last~\"       group:       \"Other\"     thalf.eff.obs:       description: NULL       text:        NULL       md:          \"t~half,eff,obs~\"       group:       \"Other\"     thalf.eff.pred:       description: NULL       text:        NULL       md:          \"t~half,eff,pred~\"       group:       \"Other\"     time_above:       # JMH how is the conconcentration specified       description: NULL       text:        NULL       md:          \"t~ab~\"       group:       \"Other\"     tlag:       description: NULL       text:        NULL       md:          \"t~lag~\"       group:       \"Other\"     tlast:       description: NULL       text:        NULL       md:          \"t~last~\"       group:       \"Other\"     vd.obs:       description: NULL       text:        NULL       md:          \"V~d,obs~\"       group:       \"Other\"     vd.pred:       description: NULL       text:        NULL       md:          \"V~d,pred~\"       group:       \"Other\"     vss.iv.last:       description: NULL       text:        NULL       md:          \"V~ss,iv,last~\"       group:       \"Other\"     vss.iv.obs:       description: NULL       text:        NULL       md:          \"V~ss,iv,obs~\"       group:       \"Other\"     vss.iv.pred:       description: NULL       text:        NULL       md:          \"V~ss,iv,pred~\"       group:       \"Other\"     vss.last:       description: NULL       text:        NULL       md:          \"V~ss,iv,last~\"       group:       \"Other\"     vss.md.obs:       description: NULL       text:        NULL       md:          \"V~ss,md,obs~\"       group:       \"Other\"     vss.md.pred:       description: NULL       text:        NULL       md:          \"V~ss,md,pred~\"       group:       \"Other\"     vss.obs:       description: NULL       text:        NULL       md:          \"V~ss,obs~\"       group:       \"Other\"     vss.pred:       description: NULL       text:        NULL       md:          \"V~ss,pred~\"       group:       \"Other\"     vz.obs:       description: NULL       text:        NULL       md:          \"V~z,obs~\"       group:       \"Other\"     vz.pred:       description: NULL       text:        NULL       md:          \"V~z,pred~\"       group:       \"Other\"   units:     # For reference this is the units vignette from PKNCA     #     # http://billdenney.github.io/pknca/articles/v07-unit-conversion.html     #     # Default value for whether or not to include units. This can     # be overwritten in the app.     include_units: TRUE     # Add unit options here, they will appear in the order they are     # entered. The _def below is the default when a new analysis is     # initialize. It must be in the list.     # BILL how will PKNCA handle units like µg     conc:     [\"ng/mL\",   \"µg/mL\",  \"g/L\",    \"molar\",  \"nM\"]     conc_def:  \"ng/mL\"     dose:     [\"g\",  \"mg\",  \"mg/kg\",   \"µg\", \"µg/kg\",   \"ng\",   \"ng/kg\"]     dose_def:  \"mg\"     amt:      [\"g\",  \"mg\", \"µg\",  \"ng\", \"g\", \"moles\", \"µmoles\", \"nmoles\"]     amt_def:   \"mg\"     time:     [\"day\",     \"hr\",      \"min\",    \"sec\"         ]     time_def:  \"day\"     vol:      [\"mL\",     \"L\"]   # This is used to generate documentation about required columns in datasets.   # Yous hould only change the Column and Description elements. The others are   # used internally.   data_format:     columns:      - entry:         sname:       \"id\"         Column:      \"Subject ID\"         Required:    \"yes\"         Description: \"Unique subject identifier.\"      - entry:         sname:       \"time\"         Column:      \"Time\"         Required:    \"yes\"         Description: \"Time since the first dose. Must be numeric.\"      - entry:         sname:       \"ntime\"         Column:      \"Nominal Time\"         Required:    \"yes\"         Description: \"Time since the last dose in the cycle (same units as Time). Must be numeric.\"      - entry:         sname:       \"conc\"         Column:      \"Concentration\"         Required:    \"yes\"         Description: \"Observed concentration. Must be numeric, no NA values. Missing values should be set to 0.\"      # BILL should this be the dose given at time zero or total dose given over a cycle      - entry:         sname:       \"dose\"         Column:      \"Dose\"         Required:    \"yes\"         Description: \"Value of dose given (must be numeric).\"      - entry:         sname:       \"route\"         Column:      \"Route\"         Required:    \"yes\"         Description: \"Doseing route. It should be either 'extravascular' or 'intravascular' but ruminate will make an effort to figure out other values.\"      - entry:         sname:       \"dur\"         Column:      \"Duration (optional)\"         Required:    \"no\"         Description: \"Doseing duration (same units as Time). If set to N/A a value of 0 will be assumed.\"      - entry:         sname:       \"cycle\"         Column:      \"Dose cycle\"         Required:    \"yes\"         Description: \"Current dose cycle\""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"creating-subjects","dir":"Articles > Rmdhunks","previous_headings":"","what":"Creating subjects","title":"","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"covariates","dir":"Articles > Rmdhunks","previous_headings":"Creating subjects","what":"Covariates","title":"","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/articles/rmdhunks/simulate_rules.html","id":"rule-based-simulations","dir":"Articles > Rmdhunks","previous_headings":"","what":"Rule-based simulations","title":"","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"rules","dir":"Articles > Rmdhunks","previous_headings":"Rule-based simulations","what":"Rules","title":"","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"rule-evaluation-environment","dir":"Articles > Rmdhunks","previous_headings":"Rule-based simulations","what":"Rule-evaluation environment","title":"","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"the-following-functions-will-be-available","dir":"Articles > Rmdhunks","previous_headings":"Rule-based simulations > Rule-evaluation environment","what":"The following functions will be available:","title":"","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"John Harrold. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Harrold J (2024). ruminate: Pharmacometrics Data Transformation Analysis Tool. R package version 0.2.3, https://ruminate.ubiquity.tools/.","code":"@Manual{,   title = {ruminate: A Pharmacometrics Data Transformation and Analysis Tool},   author = {John Harrold},   year = {2024},   note = {R package version 0.2.3},   url = {https://ruminate.ubiquity.tools/}, }"},{"path":"/index.html","id":"ruminate-","dir":"","previous_headings":"","what":"A Pharmacometrics Data Transformation and Analysis Tool","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"goal ruminate facilitate exploration pharmacometrics data. done creating Shiny interface different tools data transformation (dplyr tidyr), plotting (ggplot2), noncompartmental analysis (PKNCA). results can reported Excel, Word PowerPoint. state app can saved loaded later date. saved, script generated reproduce different actions Shiny interface.","code":""},{"path":"/index.html","id":"deployed-example-in-the-cloud","dir":"","previous_headings":"","what":"Deployed example in the cloud","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"can point web browser runrumiate.ubiquity.tools try development version ruminate. Keep mind public server. data tracked kept, open public. careful uploading confidential data. Also know single instance many people using can slow.","code":""},{"path":[]},{"path":"/index.html","id":"from-cran","dir":"","previous_headings":"","what":"From CRAN","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"can install released version rumiante CRAN :","code":"# General dependencies: install.packages(\"clipr\") install.packages(\"gridExtra\") install.packages(\"prompter\") install.packages(\"readxl\") install.packages(\"shinydashboard\") install.packages(\"ubiquity\")  # Dependencies for the MB and CTS modules install.packages(\"nlmixr2lib\") install.packages(\"nonmem2rx\") install.packages(\"rxode2\")  # Actual package install.packages(\"ruminate\")"},{"path":"/index.html","id":"development-version","dir":"","previous_headings":"","what":"Development version","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"can install development version GitHub following: Note ruminate depends formods onbrand need first install development versions onbrand formods.","code":"# Installing devtools if it's not already installed if(system.file(package=\"devtools\") == \"\"){   install.packages(\"devtools\")  }  # General dependencies: install.packages(\"clipr\") install.packages(\"gridExtra\") install.packages(\"prompter\") install.packages(\"readxl\") install.packages(\"shinydashboard\") install.packages(\"ubiquity\")  # Dependencies for the MB and CTS modules install.packages(\"nlmixr2lib\") install.packages(\"nonmem2rx\") install.packages(\"rxode2\")  devtools::install_github(\"john-harrold/onbrand\",  dependencies=TRUE) devtools::install_github(\"john-harrold/formods\",  dependencies=TRUE) devtools::install_github(\"john-harrold/ruminate\")"},{"path":[]},{"path":"/index.html","id":"in-rstudio","dir":"","previous_headings":"","what":"In RStudio","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"","code":"library(ruminate) ruminate()"},{"path":"/index.html","id":"in-a-docker-container","dir":"","previous_headings":"","what":"In a Docker container","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"Note uses x86 image. work Intel AMD cpus. means wont get far M1 mac. want, can download Docker file . build docker image repository GitHub. run Docker image locally: everything worked can point web browser : push image DockerHub: can use pull image DockerHub:","code":"docker build -t ruminate --no-cache  https://raw.githubusercontent.com/john-harrold/ruminate/main/inst/docker/local_container docker run --name ruminate --rm -p 3838:3838 ruminate http://127.0.0.1:3838/ docker tag ruminate <username>/ruminate:latest docker push <username>/ruminate:latest docker pull <username>/ruminate:latest"},{"path":"/reference/CTS_Server.html","id":null,"dir":"Reference","previous_headings":"","what":"Clinical Trial Simulator State Server — CTS_Server","title":"Clinical Trial Simulator State Server — CTS_Server","text":"Server function Clinical Trial Simulator  Shiny Module","code":""},{"path":"/reference/CTS_Server.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clinical Trial Simulator State Server — CTS_Server","text":"","code":"CTS_Server(   id,   id_ASM = \"ASM\",   id_MB = \"MB\",   FM_yaml_file = system.file(package = \"formods\", \"templates\", \"formods.yaml\"),   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"CTS.yaml\"),   deployed = FALSE,   react_state = NULL )"},{"path":"/reference/CTS_Server.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clinical Trial Simulator State Server — CTS_Server","text":"id ID string corresponds ID used call modules UI elements id_ASM ID string app state managment module used save load app states id_MB ID string corresponds ID used call MB modules FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. deployed Boolean variable indicating whether app deployed . react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/CTS_Server.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clinical Trial Simulator State Server — CTS_Server","text":"UD Server object","code":""},{"path":"/reference/CTS_add_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Covariate to Elemetnt — CTS_add_covariate","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"Takes ui elements module state processes covariate elements addition.","code":""},{"path":"/reference/CTS_add_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"","code":"CTS_add_covariate(state, element)"},{"path":"/reference/CTS_add_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_add_covariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"Element results adding covariate. cares list element can used determine exit status function. COV_IS_GOOD  TRUE covariate good added, FALSE issues. msgs     Vector messages.","code":""},{"path":"/reference/CTS_add_covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"depends following UI values state state[\"CTS\"][\"ui\"][\"covariate_value\"] state[\"CTS\"][\"ui\"][\"covariate_type_selected\"] state[\"CTS\"][\"ui\"][\"selected_covariate\"]","code":""},{"path":"/reference/CTS_add_covariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_add_rule.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Rule to Element — CTS_add_rule","title":"Add Rule to Element — CTS_add_rule","text":"Takes ui elements state element attempts add rule.","code":""},{"path":"/reference/CTS_add_rule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Rule to Element — CTS_add_rule","text":"","code":"CTS_add_rule(state, element)"},{"path":"/reference/CTS_add_rule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Rule to Element — CTS_add_rule","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_add_rule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Rule to Element — CTS_add_rule","text":"Element results adding rule. rares list element can used determine exit status function. RULE_IS_GOOD  true indicates pieces rule UI check . RULE_UPDATED  RULE_IS_GOOD RULE_UPDATED true previous rule definition overwritten. RULE_IS_GOOD TRUE RULE_UPDATED FALSE new rule added. notify_text  Text notify message notify_id    Notification ID notify_type  Notification type msgs         Vector messages.","code":""},{"path":"/reference/CTS_add_rule.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Rule to Element — CTS_add_rule","text":"depends following UI values state element state[\"CTS\"][\"ui\"][\"rule_name\"] state[\"CTS\"][\"ui\"][\"rule_condition\"] state[\"CTS\"][\"ui\"][\"rule_type\"] rule type \"dose\" state[\"CTS\"][\"ui\"][\"action_dosing_state\"] state[\"CTS\"][\"ui\"][\"action_dosing_values\"] state[\"CTS\"][\"ui\"][\"action_dosing_times\"] state[\"CTS\"][\"ui\"][\"action_dosing_durations\"] rule type \"set state\" state[\"CTS\"][\"ui\"][\"action_set_state_state\"] state[\"CTS\"][\"ui\"][\"action_set_state_values\"] rule type \"manual code\" state[\"CTS\"][\"ui\"][\"action_manual_code\"]","code":""},{"path":"/reference/CTS_add_rule.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Rule to Element — CTS_add_rule","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_append_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Report Elements — CTS_append_report","title":"Append Report Elements — CTS_append_report","text":"Appends report elements formods report.","code":""},{"path":"/reference/CTS_append_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Report Elements — CTS_append_report","text":"","code":"CTS_append_report(state, rpt, rpttype, gen_code_only = FALSE)"},{"path":"/reference/CTS_append_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Report Elements — CTS_append_report","text":"state CTS state CTS_fetch_state() rpt Report current content report appended function. details structure see documentation template_details rpttype Type report generate (supported \"xlsx\", \"pptx\", \"docx\"). gen_code_only Boolean value indicating code generated (FALSE).","code":""},{"path":"/reference/CTS_append_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Report Elements — CTS_append_report","text":"list containing following elements isgood:    Return status function. hasrptele: Boolean indicator module reportable elements. code:      Code generate reporting elements. msgs:      Messages passed back user. rpt:       Report additions passed back user.","code":""},{"path":[]},{"path":"/reference/CTS_change_source_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Change the Source Model — CTS_change_source_model","title":"Change the Source Model — CTS_change_source_model","text":"Takes ui elements state element processes changes source model updates element accordingly.","code":""},{"path":"/reference/CTS_change_source_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change the Source Model — CTS_change_source_model","text":"","code":"CTS_change_source_model(state, element)"},{"path":"/reference/CTS_change_source_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change the Source Model — CTS_change_source_model","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_change_source_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change the Source Model — CTS_change_source_model","text":"Element necessary changes source model.","code":""},{"path":"/reference/CTS_change_source_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Change the Source Model — CTS_change_source_model","text":"depends following UI values state. state[\"CTS\"][\"ui\"][\"source_model\"]","code":""},{"path":"/reference/CTS_change_source_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change the Source Model — CTS_change_source_model","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_del_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Deletes Current cohort — CTS_del_current_element","title":"Deletes Current cohort — CTS_del_current_element","text":"Takes CTS state deletes current cohort. last element, new default added.","code":""},{"path":"/reference/CTS_del_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deletes Current cohort — CTS_del_current_element","text":"","code":"CTS_del_current_element(state)"},{"path":"/reference/CTS_del_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deletes Current cohort — CTS_del_current_element","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_del_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deletes Current cohort — CTS_del_current_element","text":"CTS state object current cohort deleted.","code":""},{"path":"/reference/CTS_del_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deletes Current cohort — CTS_del_current_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_fetch_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Code — CTS_fetch_code","title":"Fetch Module Code — CTS_fetch_code","text":"Fetches code generate results seen app","code":""},{"path":"/reference/CTS_fetch_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Code — CTS_fetch_code","text":"","code":"CTS_fetch_code(state)"},{"path":"/reference/CTS_fetch_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Code — CTS_fetch_code","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Code — CTS_fetch_code","text":"Character object vector lines code","code":""},{"path":"/reference/CTS_fetch_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Code — CTS_fetch_code","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_fetch_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Current cohort — CTS_fetch_current_element","title":"Fetches Current cohort — CTS_fetch_current_element","text":"Takes CTS state returns current active cohort","code":""},{"path":"/reference/CTS_fetch_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Current cohort — CTS_fetch_current_element","text":"","code":"CTS_fetch_current_element(state)"},{"path":"/reference/CTS_fetch_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Current cohort — CTS_fetch_current_element","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Current cohort — CTS_fetch_current_element","text":"List containing details active data view. structure list structure state$CTS$elements output CTS_fetch_state().","code":""},{"path":"/reference/CTS_fetch_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Current cohort — CTS_fetch_current_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_fetch_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"Fetches datasets contained module.","code":""},{"path":"/reference/CTS_fetch_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"","code":"CTS_fetch_ds(state)"},{"path":"/reference/CTS_fetch_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"Character object vector lines code list containing following elements isgood:    Return status function. hasds:     Boolean indicator module datasets msgs:      Messages passed back user. ds:        List datasets. list element name R-object dataset. element following structure: label: Text label dataset MOD_TYPE: Short name type module. id: module ID DS: Dataframe containing actual dataset. DSMETA: Metadata describing DS code: Complete code build dataset. checksum: Module checksum. DSchecksum: Dataset checksum.","code":""},{"path":"/reference/CTS_fetch_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"","code":"# We need a module state: sess_res = CTS_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 26cc561933399401beb815326445fdcd #> → MB: module checksum updated:3a7eaf3460a9f8ed4f6962eec3be80c2 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: c499d1b8aa1202cd5d49e9985c47e057 #> → MB: module checksum updated:ea7a2403d31e0510338cd097a537d09d #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3 #> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f #> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 16bbb3aeea9892cf6a53a06e3666ff5c #> → CTS: module checksum updated: 0972fd72a2d40171e7df04cff4ce9d34 #> → CTS: source model change detected #> → CTS:  > covariates reset #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: 384629ad91b10777e86333780047de11 #> → CTS: module checksum updated: 8f3832337cda6fcfb49bcac8c62d91ae #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #> → CTS: cohort checksum updated: 128f7e0a040a7eb62ee2e7ef0b8557b9 #> → CTS: module checksum updated: 0c3803f00431fb44fbc1020644b26ef2 state = sess_res$state  ds = CTS_fetch_ds(state)  ds #> $hasds #> [1] FALSE #>  #> $isgood #> [1] TRUE #>  #> $msgs #> NULL #>  #> $ds #> list() #>"},{"path":"/reference/CTS_fetch_mdl.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"Fetches models contained module.","code":""},{"path":"/reference/CTS_fetch_mdl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"","code":"CTS_fetch_mdl(state)"},{"path":"/reference/CTS_fetch_mdl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_mdl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"list containing following elements isgood:    Return status function. hasmdl:    Boolean indicator module models msgs:      Messages passed back user. mdl:       List models. list element name R-object dataset. element following structure: label:      Text label model (e.g. one-compartment model). MOD_TYPE:   Type module. id:         Module ID. rx_obj:     rxode2 object name holds model. fcn_def:    Text define model MDLMETA:    Notes model. code:       Code generate model. checksum:   Module checksum. MDLchecksum: Model checksum.","code":""},{"path":"/reference/CTS_fetch_mdl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"","code":"# We need a module state: sess_res = CTS_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> Error in has_updated(old_chk, new_chk): could not find function \"has_updated\" state = sess_res$state #> Error in eval(expr, envir, enclos): object 'sess_res' not found  mdls = CTS_fetch_mdl(state) #> Error in CTS_fetch_mdl(state): object 'state' not found  names(mdls) #> Error in eval(expr, envir, enclos): object 'mdls' not found"},{"path":"/reference/CTS_fetch_sc_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"provides meta information simulatino options. includes option names, text descriptions, ui_names used, etc.","code":""},{"path":"/reference/CTS_fetch_sc_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"","code":"CTS_fetch_sc_meta(   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"CTS.yaml\") )"},{"path":"/reference/CTS_fetch_sc_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"MOD_yaml_file Module configuration file MC main section.","code":""},{"path":"/reference/CTS_fetch_sc_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"List following elements: config List YAML->MC->sim_config. summary: Dataframe elements config tabular format. ui_config Vector  ui_ids configuration options.","code":""},{"path":"/reference/CTS_fetch_sc_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"","code":"CTS_fetch_sc_meta() #> $config #> $config$atol #> $config$atol$group #> [1] \"ODE Solver\" #>  #> $config$atol$label #> [1] \"Absolute tolerance\" #>  #> $config$atol$tooltip #> [1] \"Absolute tolerance used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $config$atol$uitype #> [1] \"text\" #>  #> $config$atol$type #> [1] \"numeric\" #>  #> $config$atol$value #> [1] \"1e-8\" #>  #> $config$atol$options #> NULL #>  #> $config$atol$use #> [1] \"rxSolve\" #>  #> $config$atol$placeholder #> [1] \"1e-8\" #>  #> $config$atol$ui #> [1] \"cts_config_atol\" #>  #>  #> $config$rtol #> $config$rtol$group #> [1] \"ODE Solver\" #>  #> $config$rtol$label #> [1] \"Relative tolerance\" #>  #> $config$rtol$tooltip #> [1] \"Relative tolerance used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $config$rtol$uitype #> [1] \"text\" #>  #> $config$rtol$type #> [1] \"numeric\" #>  #> $config$rtol$value #> [1] \"1e-6\" #>  #> $config$rtol$options #> NULL #>  #> $config$rtol$use #> [1] \"rxSolve\" #>  #> $config$rtol$placeholder #> [1] \"1e-6\" #>  #> $config$rtol$ui #> [1] \"cts_config_rtol\" #>  #>  #> $config$sigdig #> $config$sigdig$group #> [1] \"ODE Solver\" #>  #> $config$sigdig$label #> [1] \"Significant digits\" #>  #> $config$sigdig$tooltip #> [1] \"Specifies the \\\"significant digits\\\" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\\\\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\\\\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\\\\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.\" #>  #> $config$sigdig$uitype #> [1] \"text\" #>  #> $config$sigdig$type #> [1] \"numeric\" #>  #> $config$sigdig$value #> [1] \"NULL\" #>  #> $config$sigdig$options #> NULL #>  #> $config$sigdig$use #> [1] \"rxSolve\" #>  #> $config$sigdig$placeholder #> [1] \"NULL\" #>  #> $config$sigdig$ui #> [1] \"cts_config_sigdig\" #>  #>  #> $config$seed #> $config$seed$group #> [1] \"Misc\" #>  #> $config$seed$label #> [1] \"Random Seed\" #>  #> $config$seed$tooltip #> [1] \"Integer value to set the seed for the random number generator\" #>  #> $config$seed$uitype #> [1] \"text\" #>  #> $config$seed$type #> [1] \"numeric\" #>  #> $config$seed$value #> [1] \"86753090\" #>  #> $config$seed$options #> NULL #>  #> $config$seed$use #> [1] \"Misc\" #>  #> $config$seed$placeholder #> [1] \"8675309\" #>  #> $config$seed$ui #> [1] \"cts_config_seed\" #>  #>  #> $config$nsteps #> $config$nsteps$group #> [1] \"Misc\" #>  #> $config$nsteps$label #> [1] \"Time Steps\" #>  #> $config$nsteps$tooltip #> [1] \"Integer value representing the number of time steps to add between zero and the end of trial. A larger value will give smoother curves but it will also take more time to simulate.\" #>  #> $config$nsteps$uitype #> [1] \"text\" #>  #> $config$nsteps$type #> [1] \"numeric\" #>  #> $config$nsteps$value #> [1] \"200\" #>  #> $config$nsteps$options #> NULL #>  #> $config$nsteps$use #> [1] \"Misc\" #>  #> $config$nsteps$placeholder #> [1] \"200\" #>  #> $config$nsteps$ui #> [1] \"cts_config_nsteps\" #>  #>  #> $config$preamble #> $config$preamble$group #> [1] \"Rule Environment Preamble\" #>  #> $config$preamble$label #> NULL #>  #> $config$preamble$tooltip #> [1] \"Optional user-defined code to be executed in the rule-evaluation environment. You can create user-defined functions here.\" #>  #> $config$preamble$uitype #> [1] \"textarea\" #>  #> $config$preamble$type #> [1] \"character\" #>  #> $config$preamble$value #> NULL #>  #> $config$preamble$options #> NULL #>  #> $config$preamble$use #> [1] \"rules\" #>  #> $config$preamble$placeholder #> [1] \"my_func = function(id=NULL){\\n\\n}\" #>  #> $config$preamble$ui #> [1] \"cts_config_preamble\" #>  #>  #>  #> $cfg_summary #>       name                  ui     use      type                     group #> 1     atol     cts_config_atol rxSolve   numeric                ODE Solver #> 2     rtol     cts_config_rtol rxSolve   numeric                ODE Solver #> 3   sigdig   cts_config_sigdig rxSolve   numeric                ODE Solver #> 4     seed     cts_config_seed    Misc   numeric                      Misc #> 5   nsteps   cts_config_nsteps    Misc   numeric                      Misc #> 6 preamble cts_config_preamble   rules character Rule Environment Preamble #>  #> $ui_config #> [1] \"cts_config_atol\"     \"cts_config_rtol\"     \"cts_config_sigdig\"   #> [4] \"cts_config_seed\"     \"cts_config_nsteps\"   \"cts_config_preamble\" #>"},{"path":"/reference/CTS_fetch_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Clinical Trial Simulator State — CTS_fetch_state","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"Merges default app options changes made UI","code":""},{"path":"/reference/CTS_fetch_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"","code":"CTS_fetch_state(   id,   id_ASM,   id_MB,   input,   session,   FM_yaml_file,   MOD_yaml_file,   react_state )"},{"path":"/reference/CTS_fetch_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"id Shiny module ID id_ASM ID string app state management module used save load app states id_MB ID string corresponds ID used call MB modules input Shiny input variable session Shiny session variable FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/CTS_fetch_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. CTS: isgood: Boolean object indicating file successfully loaded. checksum: MD5 sum contents element can used detect changes state. MOD_TYPE: Character data containing type module \"CTS\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/CTS_fetch_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = CTS_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3 #> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f #> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 224113a90fd21bfb2af49de3214fb2a2 #> → CTS: module checksum updated: 437596b28f915895d6a884ba41e1c3e1 #> → CTS: source model change detected #> → CTS:  > covariates reset #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: 384629ad91b10777e86333780047de11 #> → CTS: module checksum updated: 475d2d0e93115c5a1924b97fcfd04e6a #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #> → CTS: cohort checksum updated: 6d48d1dd9570433063d16767b8aab7cb #> → CTS: module checksum updated: f5cfc55ad123ff198550822db26c76d6 session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"CTS.yaml\")  # Creating an empty state object state = CTS_fetch_state(id              = \"CTS\",                        id_ASM          = \"ASM\",                        id_MB           = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)"},{"path":"/reference/CTS_init_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize CTS Module State — CTS_init_state","title":"Initialize CTS Module State — CTS_init_state","text":"Creates list initialized module state","code":""},{"path":"/reference/CTS_init_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize CTS Module State — CTS_init_state","text":"","code":"CTS_init_state(FM_yaml_file, MOD_yaml_file, id, id_MB, session)"},{"path":"/reference/CTS_init_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize CTS Module State — CTS_init_state","text":"FM_yaml_file App configuration file FM main section MOD_yaml_file Module configuration file MC main section id ID string module id_MB ID string corresponds ID used call MB modules session Shiny session variable","code":""},{"path":"/reference/CTS_init_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize CTS Module State — CTS_init_state","text":"list containing empty CTS state","code":""},{"path":"/reference/CTS_init_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize CTS Module State — CTS_init_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = CTS_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3 #> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f #> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: e9f6c8e3b3aac0f67b7ba01990565c45 #> → CTS: module checksum updated: b91782c28c672958b500b0aa26f62ebe #> → CTS: source model change detected #> → CTS:  > covariates reset #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: 384629ad91b10777e86333780047de11 #> → CTS: module checksum updated: eb776cbacab36d2d2bb2d13c6c66d750 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #> → CTS: cohort checksum updated: a0ea53dbc455f2b03311669c0c19a5ea #> → CTS: module checksum updated: b07c64e0db4d0316e05e134a6369112f session = sess_res$session input   = sess_res$input  state = CTS_init_state(    FM_yaml_file  = system.file(package = \"formods\",                                \"templates\",                                \"formods.yaml\"),    MOD_yaml_file = system.file(package = \"ruminate\",                                \"templates\",                                \"CTS.yaml\"),    id              = \"CTS\",    id_MB           = \"MB\",    session         = session) #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized  state #> $yaml #> $yaml$FM #> $yaml$FM$include #> $yaml$FM$include$files #> $yaml$FM$include$files[[1]] #> $yaml$FM$include$files[[1]]$file #> $yaml$FM$include$files[[1]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.docx\\\")\" #>  #> $yaml$FM$include$files[[1]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.docx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[2]] #> $yaml$FM$include$files[[2]]$file #> $yaml$FM$include$files[[2]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.pptx\\\")\" #>  #> $yaml$FM$include$files[[2]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.pptx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[3]] #> $yaml$FM$include$files[[3]]$file #> $yaml$FM$include$files[[3]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.yaml\\\")\" #>  #> $yaml$FM$include$files[[3]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.yaml\\\")\" #>  #>  #>  #>  #>  #> $yaml$FM$deployed #> [1] FALSE #>  #> $yaml$FM$code #> $yaml$FM$code$theme #> [1] \"vibrant_ink\" #>  #> $yaml$FM$code$showLineNumbers #> [1] TRUE #>  #> $yaml$FM$code$gen_file #> [1] \"run_analysis.R\" #>  #> $yaml$FM$code$gen_preamble #> [1] \"# formods automated output ------------------------------------------------\\n# https://formods.ubiquity.tools/\\nrm(list=ls())\" #>  #> $yaml$FM$code$packages #> [1] \"onbrand\" \"writexl\" #>  #>  #> $yaml$FM$notifications #> $yaml$FM$notifications$config #> $yaml$FM$notifications$config$success #> $yaml$FM$notifications$config$success$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$background #> [1] \"#5bb85b\" #>  #>  #> $yaml$FM$notifications$config$failure #> $yaml$FM$notifications$config$failure$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$background #> [1] \"#d9534f\" #>  #>  #> $yaml$FM$notifications$config$info #> $yaml$FM$notifications$config$info$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$background #> [1] \"#5bc0de\" #>  #>  #> $yaml$FM$notifications$config$warning #> $yaml$FM$notifications$config$warning$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$background #> [1] \"#f0ac4d\" #>  #>  #>  #>  #> $yaml$FM$reporting #> $yaml$FM$reporting$enabled #> [1] TRUE #>  #> $yaml$FM$reporting$content_init #> $yaml$FM$reporting$content_init$xlsx #> [1] \"rpt = list(summary = NULL,\\n           sheets  = list())\" #>  #> $yaml$FM$reporting$content_init$docx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.docx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #> $yaml$FM$reporting$content_init$pptx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.pptx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #>  #>  #> $yaml$FM$ui #> $yaml$FM$ui$button_style #> [1] \"fill\" #>  #> $yaml$FM$ui$select_size #> [1] 10 #>  #> $yaml$FM$ui$color_green #> [1] \"#00BB8A\" #>  #> $yaml$FM$ui$color_red #> [1] \"#FF475E\" #>  #> $yaml$FM$ui$color_blue #> [1] \"#0088FF\" #>  #> $yaml$FM$ui$color_purple #> [1] \"#bd2cf4\" #>  #>  #> $yaml$FM$data_meta #> $yaml$FM$data_meta$data_header #> [1] \"<span style='color:===COLOR==='><b>===NAME===<\/b><br/><font size='-3'>===LABEL===<\/font><\/span>\" #>  #> $yaml$FM$data_meta$subtext #> [1] \"===LABEL===: ===RANGE===\" #>  #> $yaml$FM$data_meta$many_sep #> [1] \",⋅⋅⋅,\" #>  #> $yaml$FM$data_meta$data_types #> $yaml$FM$data_meta$data_types$character #> $yaml$FM$data_meta$data_types$character$color #> [1] \"#DD4B39\" #>  #> $yaml$FM$data_meta$data_types$character$label #> [1] \"text\" #>  #>  #> $yaml$FM$data_meta$data_types$double #> $yaml$FM$data_meta$data_types$double$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$double$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$integer #> $yaml$FM$data_meta$data_types$integer$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$integer$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$other #> $yaml$FM$data_meta$data_types$other$color #> [1] \"black\" #>  #> $yaml$FM$data_meta$data_types$other$label #> [1] \"other\" #>  #>  #>  #>  #> $yaml$FM$labels #> $yaml$FM$labels$default_ds #> [1] \"Original data set\" #>  #>  #> $yaml$FM$user_files #> $yaml$FM$user_files$use_tmpdir #> [1] TRUE #>  #>  #> $yaml$FM$logging #> $yaml$FM$logging$enabled #> [1] TRUE #>  #> $yaml$FM$logging$timestamp #> [1] TRUE #>  #> $yaml$FM$logging$timestamp_fmt #> [1] \"%Y-%m-%d %H:%M:%S\" #>  #> $yaml$FM$logging$log_file #> [1] \"formods_log.txt\" #>  #> $yaml$FM$logging$console #> [1] TRUE #>  #>  #>  #>  #> $MC #> $MC$element_object_name #> [1] \"CTS_obj\" #>  #> $MC$code #> $MC$code$packages #> [1] \"rxode2\"     \"nonmem2rx\"  \"nlmixr2lib\" #>  #> $MC$code$readOnly #> [1] TRUE #>  #> $MC$code$mode #> [1] \"r\" #>  #>  #> $MC$compact #> $MC$compact$code #> [1] TRUE #>  #> $MC$compact$clip #> [1] TRUE #>  #>  #> $MC$reporting #> $MC$reporting$enabled #> [1] FALSE #>  #> $MC$reporting$priority #> [1] 1 #>  #>  #> $MC$formatting #> $MC$formatting$code #> $MC$formatting$code$width #> [1] 800 #>  #> $MC$formatting$code$height #> [1] 300 #>  #>  #> $MC$formatting$current_element #> $MC$formatting$current_element$width #> [1] \"200px\" #>  #>  #> $MC$formatting$source_model #> $MC$formatting$source_model$width #> [1] \"200px\" #>  #>  #> $MC$formatting$element_name #> $MC$formatting$element_name$width #> [1] \"200px\" #>  #>  #> $MC$formatting$preview #> $MC$formatting$preview$width #> [1] \"800px\" #>  #> $MC$formatting$preview$height #> [1] \"500px\" #>  #>  #> $MC$formatting$input #> NULL #>  #> $MC$formatting$button_clk_run #> $MC$formatting$button_clk_run$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_run$block #> [1] TRUE #>  #>  #> $MC$formatting$button_clk_del #> $MC$formatting$button_clk_del$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_del$block #> [1] TRUE #>  #> $MC$formatting$button_clk_del$tooltip #> [1] \"Delete the current cohort.\" #>  #> $MC$formatting$button_clk_del$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_save #> $MC$formatting$button_clk_save$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_save$block #> [1] TRUE #>  #> $MC$formatting$button_clk_save$tooltip #> [1] \"Save the name for the current cohort.\" #>  #> $MC$formatting$button_clk_save$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_runsim #> $MC$formatting$button_clk_runsim$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_runsim$block #> [1] TRUE #>  #> $MC$formatting$button_clk_runsim$tooltip #> [1] \"Run the current simulation.\" #>  #> $MC$formatting$button_clk_runsim$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_update_plot #> $MC$formatting$button_clk_update_plot$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_update_plot$block #> [1] TRUE #>  #> $MC$formatting$button_clk_update_plot$tooltip #> [1] \"Update timecourse figures.\" #>  #> $MC$formatting$button_clk_update_plot$tooltip_position #> [1] \"top\" #>  #>  #> $MC$formatting$button_clk_clip #> $MC$formatting$button_clk_clip$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_clip$block #> [1] TRUE #>  #> $MC$formatting$button_clk_clip$tooltip #> [1] \"Copy the code to generate the current cohort to the clipboard.\" #>  #> $MC$formatting$button_clk_clip$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_copy #> $MC$formatting$button_clk_copy$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_copy$block #> [1] TRUE #>  #> $MC$formatting$button_clk_copy$tooltip #> [1] \"Make a copy of the current cohort.\" #>  #> $MC$formatting$button_clk_copy$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_new #> $MC$formatting$button_clk_new$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_new$block #> [1] TRUE #>  #> $MC$formatting$button_clk_new$tooltip #> [1] \"Create a new cohort.\" #>  #> $MC$formatting$button_clk_new$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_add_rule #> $MC$formatting$button_clk_add_rule$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_add_rule$block #> [1] FALSE #>  #> $MC$formatting$button_clk_add_rule$tooltip #> [1] \"Add a new rule.\" #>  #> $MC$formatting$button_clk_add_rule$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_add_cov #> $MC$formatting$button_clk_add_cov$width #> [1] \"456px\" #>  #> $MC$formatting$button_clk_add_cov$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_add_cov$block #> [1] TRUE #>  #> $MC$formatting$button_clk_add_cov$tooltip #> [1] \"Define covariate.\" #>  #> $MC$formatting$button_clk_add_cov$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$rule_condition #> $MC$formatting$rule_condition$width #> [1] 300 #>  #> $MC$formatting$rule_condition$height #> [1] 100 #>  #> $MC$formatting$rule_condition$tooltip #> [1] \"This is a condition that should evaluate to either TRUE (if the action should be taken) or FALSE (if it should be skipped).\" #>  #> $MC$formatting$rule_condition$tooltip_position #> [1] \"bottom\" #>  #> $MC$formatting$rule_condition$placeholder #> [1] \"examples: TRUE will run the action at at every visit or (time > 0) & (Ac < 10) will be more selective.\" #>  #>  #> $MC$formatting$action_dosing_state #> $MC$formatting$action_dosing_state$width #> [1] 200 #>  #>  #> $MC$formatting$action_dosing_values #> $MC$formatting$action_dosing_values$width #> [1] 200 #>  #> $MC$formatting$action_dosing_values$placeholder #> [1] \"c(1, 1, 1)\" #>  #> $MC$formatting$action_dosing_values$tooltip #> [1] \"Expression defining dosing amounts that will evaluate as a numeric vector of numbers.\" #>  #>  #> $MC$formatting$action_dosing_times #> $MC$formatting$action_dosing_times$width #> [1] 200 #>  #> $MC$formatting$action_dosing_times$placeholder #> [1] \"c(0, 7, 14)\" #>  #> $MC$formatting$action_dosing_times$tooltip #> [1] \"Expression defining dosing times that will evaluate as a numeric vector of numbers.\" #>  #> $MC$formatting$action_dosing_times$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$action_dosing_durations #> $MC$formatting$action_dosing_durations$width #> [1] 200 #>  #> $MC$formatting$action_dosing_durations$placeholder #> [1] \"c(0, 0, 0)\" #>  #> $MC$formatting$action_dosing_durations$tooltip #> [1] \"Expression containing dosing duration that will evaluate as a numeric vector of numbers (zeros for bolus dosing).\" #>  #> $MC$formatting$action_dosing_durations$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$action_set_state_state #> $MC$formatting$action_set_state_state$width #> [1] 200 #>  #>  #> $MC$formatting$action_set_state_value #> $MC$formatting$action_set_state_value$width #> [1] 200 #>  #> $MC$formatting$action_set_state_value$placeholder #> [1] \"A value like 20 or an expression in terms of a system element such as Ac/2\" #>  #> $MC$formatting$action_set_state_value$tooltip #> [1] \"An expression that evaluates to a new number.\" #>  #>  #> $MC$formatting$action_manual_code #> $MC$formatting$action_manual_code$width #> [1] 200 #>  #> $MC$formatting$action_manual_code$height #> [1] 75 #>  #> $MC$formatting$action_manual_code$placeholder #> [1] \"SI_interval_ev = etRbind(SI_interval_ev,\\n  et(cmt  = 'Ac',\\n     id   = id,\\n     amt  = 10,\\n     evid = 4,\\n     time = time))\" #>  #> $MC$formatting$action_manual_code$tooltip #> [1] \"Expression with code to manually update the event table.\" #>  #>  #> $MC$formatting$rule_type #> $MC$formatting$rule_type$width #> [1] 100 #>  #> $MC$formatting$rule_type$options #> $MC$formatting$rule_type$options$dose #> $MC$formatting$rule_type$options$dose$choice #> [1] \"Dosing\" #>  #> $MC$formatting$rule_type$options$dose$subtext #> [1] \"Add bolus or infusion dosing\" #>  #> $MC$formatting$rule_type$options$dose$value #> [1] \"dose\" #>  #>  #> $MC$formatting$rule_type$options$state_reset #> $MC$formatting$rule_type$options$state_reset$choice #> [1] \"Set State\" #>  #> $MC$formatting$rule_type$options$state_reset$subtext #> [1] \"Reset the named state or compartment to a specified value\" #>  #> $MC$formatting$rule_type$options$state_reset$value #> [1] \"set state\" #>  #>  #> $MC$formatting$rule_type$options$manual #> $MC$formatting$rule_type$options$manual$choice #> [1] \"Manual\" #>  #> $MC$formatting$rule_type$options$manual$subtext #> [1] \"Alter the event table directly.\" #>  #> $MC$formatting$rule_type$options$manual$value #> [1] \"manual\" #>  #>  #>  #>  #> $MC$formatting$config_text #> $MC$formatting$config_text$width #> [1] 150 #>  #>  #> $MC$formatting$config_select #> $MC$formatting$config_select$width #> [1] 150 #>  #>  #> $MC$formatting$config_textarea #> $MC$formatting$config_textarea$width #> [1] 456 #>  #> $MC$formatting$config_textarea$height #> [1] 150 #>  #>  #> $MC$formatting$hot_current_rules #> $MC$formatting$hot_current_rules$width #> [1] 800 #>  #> $MC$formatting$hot_current_rules$height #> [1] 400 #>  #> $MC$formatting$hot_current_rules$no_rules #> [1] \"No Rules Defined\" #>  #>  #> $MC$formatting$hot_current_covariates #> $MC$formatting$hot_current_covariates$width #> [1] 800 #>  #> $MC$formatting$hot_current_covariates$height #> [1] 400 #>  #> $MC$formatting$hot_current_covariates$no_covariates #> [1] \"Covariate not yet defined\" #>  #>  #> $MC$formatting$nsub #> $MC$formatting$nsub$value #> [1] 10 #>  #> $MC$formatting$nsub$width #> [1] 50 #>  #> $MC$formatting$nsub$placeholder #> [1] 10 #>  #> $MC$formatting$nsub$tooltip #> [1] \"Number of sujbects in the current cohort.\" #>  #> $MC$formatting$nsub$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$visit_times #> $MC$formatting$visit_times$value #> [1] 0 #>  #> $MC$formatting$visit_times$width #> [1] 150 #>  #> $MC$formatting$visit_times$placeholder #> [1] \"c(0, 7, 14, 21)\" #>  #> $MC$formatting$visit_times$tooltip #> [1] \"Study visit times.\" #>  #> $MC$formatting$visit_times$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$trial_end #> $MC$formatting$trial_end$value #> [1] 100 #>  #> $MC$formatting$trial_end$width #> [1] 150 #>  #> $MC$formatting$trial_end$placeholder #> [1] \"c(0, 7, 14, 21)\" #>  #> $MC$formatting$trial_end$tooltip #> [1] \"Study visit times.\" #>  #> $MC$formatting$trial_end$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$covariates #> $MC$formatting$covariates$none_found #> [1] \"There were no coviaraites found in this model\" #>  #>  #> $MC$formatting$selected_covariate #> $MC$formatting$selected_covariate$width #> [1] 150 #>  #> $MC$formatting$selected_covariate$tooltip #> [1] \"Pick covariate to define.\" #>  #> $MC$formatting$selected_covariate$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$rule_name #> $MC$formatting$rule_name$width #> [1] 150 #>  #> $MC$formatting$rule_name$placeholder #> [1] \"first_dose\" #>  #> $MC$formatting$rule_name$tooltip #> [1] \"This is a unique identifier for the current rule. It should start witha letter and can contain letters, numbers and underscores.\" #>  #> $MC$formatting$rule_name$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$tc_dim #> $MC$formatting$tc_dim$width #> [1] 100 #>  #> $MC$formatting$tc_dim$default #> [1] \"d2x4\" #>  #> $MC$formatting$tc_dim$tooltip #> [1] \"rows x cols for figures\" #>  #> $MC$formatting$tc_dim$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$tc_dim$choices #> $MC$formatting$tc_dim$choices$d4x4 #> $MC$formatting$tc_dim$choices$d4x4$verb #> [1] \"4x4\" #>  #> $MC$formatting$tc_dim$choices$d4x4$nrow #> [1] 4 #>  #> $MC$formatting$tc_dim$choices$d4x4$ncol #> [1] 4 #>  #>  #> $MC$formatting$tc_dim$choices$d2x4 #> $MC$formatting$tc_dim$choices$d2x4$verb #> [1] \"2x4\" #>  #> $MC$formatting$tc_dim$choices$d2x4$nrow #> [1] 2 #>  #> $MC$formatting$tc_dim$choices$d2x4$ncol #> [1] 4 #>  #>  #> $MC$formatting$tc_dim$choices$d2x2 #> $MC$formatting$tc_dim$choices$d2x2$verb #> [1] \"2x2\" #>  #> $MC$formatting$tc_dim$choices$d2x2$nrow #> [1] 2 #>  #> $MC$formatting$tc_dim$choices$d2x2$ncol #> [1] 2 #>  #>  #> $MC$formatting$tc_dim$choices$d1x1 #> $MC$formatting$tc_dim$choices$d1x1$verb #> [1] \"1x1\" #>  #> $MC$formatting$tc_dim$choices$d1x1$nrow #> [1] 1 #>  #> $MC$formatting$tc_dim$choices$d1x1$ncol #> [1] 1 #>  #>  #>  #>  #> $MC$formatting$dvcols #> $MC$formatting$dvcols$width #> [1] 150 #>  #> $MC$formatting$dvcols$size #> [1] 10 #>  #> $MC$formatting$dvcols$tooltip #> [1] \"Select the different model outputs, states, etc to plot\" #>  #> $MC$formatting$dvcols$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$dvcols$choices #> $MC$formatting$dvcols$choices$outputs #> $MC$formatting$dvcols$choices$outputs$verb #> [1] \"Outputs\" #>  #>  #> $MC$formatting$dvcols$choices$states #> $MC$formatting$dvcols$choices$states$verb #> [1] \"States\" #>  #>  #> $MC$formatting$dvcols$choices$parameters #> $MC$formatting$dvcols$choices$parameters$verb #> [1] \"Parameters\" #>  #>  #> $MC$formatting$dvcols$choices$secondary #> $MC$formatting$dvcols$choices$secondary$verb #> [1] \"Secondary Parameters\" #>  #>  #> $MC$formatting$dvcols$choices$covariates #> $MC$formatting$dvcols$choices$covariates$verb #> [1] \"Covariates\" #>  #>  #>  #>  #> $MC$formatting$evplot #> $MC$formatting$evplot$width #> [1] 150 #>  #> $MC$formatting$evplot$tooltip #> [1] \"Select the events to plot\" #>  #> $MC$formatting$evplot$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$evplot$default #> [1] \"dose\" #>  #> $MC$formatting$evplot$choices #> $MC$formatting$evplot$choices$dose #> $MC$formatting$evplot$choices$dose$value #> [1] 1 #>  #> $MC$formatting$evplot$choices$dose$verb #> [1] \"Dose\" #>  #>  #> $MC$formatting$evplot$choices$set_state #> $MC$formatting$evplot$choices$set_state$value #> [1] 4 #>  #> $MC$formatting$evplot$choices$set_state$verb #> [1] \"State Resets\" #>  #>  #>  #>  #> $MC$formatting$fpage #> $MC$formatting$fpage$width #> [1] 50 #>  #> $MC$formatting$fpage$tooltip #> [1] \"Go to the selected output page\" #>  #> $MC$formatting$fpage$tooltip_position #> [1] \"top\" #>  #>  #>  #> $MC$labels #> $MC$labels$current_element #> NULL #>  #> $MC$labels$element_name #> NULL #>  #> $MC$labels$save_btn #> [1] \"Save\" #>  #> $MC$labels$runsim_btn #> [1] \"Run Simulation\" #>  #> $MC$labels$update_plot_btn #> [1] \"Update Plots\" #>  #> $MC$labels$clip_btn #> [1] \"Code\" #>  #> $MC$labels$copy_btn #> [1] \"Copy\" #>  #> $MC$labels$del_btn #> [1] \"Delete\" #>  #> $MC$labels$add_rule_btn #> [1] \"Add Rule\" #>  #> $MC$labels$add_cov_btn #> [1] \"Add Covariate Details\" #>  #> $MC$labels$new_btn #> [1] \"New\" #>  #> $MC$labels$source_model #> [1] \"Select Model\" #>  #> $MC$labels$dvcols #> NULL #>  #> $MC$labels$evplot #> NULL #>  #> $MC$labels$rule_type #> [1] \"Type of rule\" #>  #> $MC$labels$rule_condition #> [1] \"Condition to trigger rule\" #>  #> $MC$labels$action_dosing_state #> [1] \"Dosing into\" #>  #> $MC$labels$action_dosing_values #> [1] \"Values\" #>  #> $MC$labels$action_dosing_times #> [1] \"Times\" #>  #> $MC$labels$action_manual_code #> [1] \"Code to evaluate\" #>  #> $MC$labels$action_dosing_durations #> [1] \"Durations\" #>  #> $MC$labels$action_set_state_state #> [1] \"State/compartment to reset\" #>  #> $MC$labels$action_set_state_value #> [1] \"New value for state/compartment\" #>  #> $MC$labels$nsub #> [1] \"N Subs\" #>  #> $MC$labels$visit_times #> [1] \"Visit Times\" #>  #> $MC$labels$trial_end #> [1] \"End of Trial\" #>  #> $MC$labels$selected_covariate #> [1] \"Covariate\" #>  #> $MC$labels$covariate_type #> [1] \"Type/Distribution\" #>  #> $MC$labels$covariate_value #> [1] \"Values\" #>  #> $MC$labels$switch_output_interactive #> [1] \"Interactive\" #>  #> $MC$labels$switch_output_report #> [1] \"Report Preview\" #>  #> $MC$labels$tab_res_tc_figure #> [1] \"Timecourse\" #>  #> $MC$labels$tab_res_events_figure #> [1] \"Events\" #>  #> $MC$labels$tab_sim_env #> [1] \"Simulation Environment\" #>  #>  #> $MC$errors #> $MC$errors$no_sim_found #> [1] \"You need to run the simulation first.\" #>  #> $MC$errors$bad_sim #> [1] \"There was a problem with the simulation.\" #>  #> $MC$errors$cts_no_fig #> [1] \"There are no figures to display. You must run the simulation first\" #>  #>  #> $MC$tooltips #> $MC$tooltips$include #> [1] TRUE #>  #> $MC$tooltips$elements #> [1] \"Show cohort components\" #>  #> $MC$tooltips$show_code #> [1] \"Show cohort code\" #>  #> $MC$tooltips$url_manual #> [1] \"https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html#example-manual-code-evaluation\" #>  #> $MC$tooltips$url_rule_env #> [1] \"https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html#rule-evaluation-environment\" #>  #>  #> $MC$rule_env_content #> [1] \"uiele = tagList(tags$b(\\\"Simulation environment\\\"), tags$br(),\\n                \\\"The condition and actions (e.g., dose times, amounts,\\n                durations) that are triggered are all\\n                evaluated in an environment with different objects that\\n                you can use. For details on this see the \\\", \\n                tags$a(href=state[[\\\"MC\\\"]][[\\\"tooltips\\\"]][[\\\"url_rule_env\\\"]],\\n                 \\\"Clinical Trial Simulation\\\"), \\\" vignette.\\\")\\nuiele = tagList(uiele, \\\"For the current system the following are available:\\\", tags$br())\\nuiele = tagList(uiele, tags$br())\\nuiele = tagList(uiele, rx_details[[\\\"ht_info\\\"]])\" #>  #> $MC$sim_config #> $MC$sim_config$atol #> $MC$sim_config$atol$group #> [1] \"ODE Solver\" #>  #> $MC$sim_config$atol$label #> [1] \"Absolute tolerance\" #>  #> $MC$sim_config$atol$tooltip #> [1] \"Absolute tolerance used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $MC$sim_config$atol$uitype #> [1] \"text\" #>  #> $MC$sim_config$atol$type #> [1] \"numeric\" #>  #> $MC$sim_config$atol$value #> [1] \"1e-8\" #>  #> $MC$sim_config$atol$options #> NULL #>  #> $MC$sim_config$atol$use #> [1] \"rxSolve\" #>  #> $MC$sim_config$atol$placeholder #> [1] \"1e-8\" #>  #>  #> $MC$sim_config$rtol #> $MC$sim_config$rtol$group #> [1] \"ODE Solver\" #>  #> $MC$sim_config$rtol$label #> [1] \"Relative tolerance\" #>  #> $MC$sim_config$rtol$tooltip #> [1] \"Relative tolerance used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $MC$sim_config$rtol$uitype #> [1] \"text\" #>  #> $MC$sim_config$rtol$type #> [1] \"numeric\" #>  #> $MC$sim_config$rtol$value #> [1] \"1e-6\" #>  #> $MC$sim_config$rtol$options #> NULL #>  #> $MC$sim_config$rtol$use #> [1] \"rxSolve\" #>  #> $MC$sim_config$rtol$placeholder #> [1] \"1e-6\" #>  #>  #> $MC$sim_config$sigdig #> $MC$sim_config$sigdig$group #> [1] \"ODE Solver\" #>  #> $MC$sim_config$sigdig$label #> [1] \"Significant digits\" #>  #> $MC$sim_config$sigdig$tooltip #> [1] \"Specifies the \\\"significant digits\\\" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\\\\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\\\\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\\\\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.\" #>  #> $MC$sim_config$sigdig$uitype #> [1] \"text\" #>  #> $MC$sim_config$sigdig$type #> [1] \"numeric\" #>  #> $MC$sim_config$sigdig$value #> [1] \"NULL\" #>  #> $MC$sim_config$sigdig$options #> NULL #>  #> $MC$sim_config$sigdig$use #> [1] \"rxSolve\" #>  #> $MC$sim_config$sigdig$placeholder #> [1] \"NULL\" #>  #>  #> $MC$sim_config$seed #> $MC$sim_config$seed$group #> [1] \"Misc\" #>  #> $MC$sim_config$seed$label #> [1] \"Random Seed\" #>  #> $MC$sim_config$seed$tooltip #> [1] \"Integer value to set the seed for the random number generator\" #>  #> $MC$sim_config$seed$uitype #> [1] \"text\" #>  #> $MC$sim_config$seed$type #> [1] \"numeric\" #>  #> $MC$sim_config$seed$value #> [1] \"86753090\" #>  #> $MC$sim_config$seed$options #> NULL #>  #> $MC$sim_config$seed$use #> [1] \"Misc\" #>  #> $MC$sim_config$seed$placeholder #> [1] \"8675309\" #>  #>  #> $MC$sim_config$nsteps #> $MC$sim_config$nsteps$group #> [1] \"Misc\" #>  #> $MC$sim_config$nsteps$label #> [1] \"Time Steps\" #>  #> $MC$sim_config$nsteps$tooltip #> [1] \"Integer value representing the number of time steps to add between zero and the end of trial. A larger value will give smoother curves but it will also take more time to simulate.\" #>  #> $MC$sim_config$nsteps$uitype #> [1] \"text\" #>  #> $MC$sim_config$nsteps$type #> [1] \"numeric\" #>  #> $MC$sim_config$nsteps$value #> [1] \"200\" #>  #> $MC$sim_config$nsteps$options #> NULL #>  #> $MC$sim_config$nsteps$use #> [1] \"Misc\" #>  #> $MC$sim_config$nsteps$placeholder #> [1] \"200\" #>  #>  #> $MC$sim_config$preamble #> $MC$sim_config$preamble$group #> [1] \"Rule Environment Preamble\" #>  #> $MC$sim_config$preamble$label #> NULL #>  #> $MC$sim_config$preamble$tooltip #> [1] \"Optional user-defined code to be executed in the rule-evaluation environment. You can create user-defined functions here.\" #>  #> $MC$sim_config$preamble$uitype #> [1] \"textarea\" #>  #> $MC$sim_config$preamble$type #> [1] \"character\" #>  #> $MC$sim_config$preamble$value #> NULL #>  #> $MC$sim_config$preamble$options #> NULL #>  #> $MC$sim_config$preamble$use #> [1] \"rules\" #>  #> $MC$sim_config$preamble$placeholder #> [1] \"my_func = function(id=NULL){\\n\\n}\" #>  #>  #>  #> $MC$covariate_generation #> $MC$covariate_generation$tooltip_position #> [1] \"right\" #>  #> $MC$covariate_generation$width #> [1] 150 #>  #> $MC$covariate_generation$types #> $MC$covariate_generation$types$discrete #> $MC$covariate_generation$types$discrete$choice #> [1] \"Select from discrete values\" #>  #> $MC$covariate_generation$types$discrete$type #> [1] \"discrete\" #>  #> $MC$covariate_generation$types$discrete$placeholder #> [1] \"0, 1, 2\" #>  #> $MC$covariate_generation$types$discrete$tooltip #> [1] \"Provide a list of possible discrete values and these will be randomly selected when genreating subjects.\" #>  #>  #> $MC$covariate_generation$types$fixed #> $MC$covariate_generation$types$fixed$choice #> [1] \"Fix at value\" #>  #> $MC$covariate_generation$types$fixed$type #> [1] \"fixed\" #>  #> $MC$covariate_generation$types$fixed$placeholder #> [1] \"1\" #>  #> $MC$covariate_generation$types$fixed$tooltip #> [1] \"Fix the covariate at a specific value.\" #>  #>  #> $MC$covariate_generation$types$cont_random #> $MC$covariate_generation$types$cont_random$choice #> [1] \"Continuous/random in range\" #>  #> $MC$covariate_generation$types$cont_random$type #> [1] \"continuous\" #>  #> $MC$covariate_generation$types$cont_random$sampling #> [1] \"random\" #>  #> $MC$covariate_generation$types$cont_random$placeholder #> [1] \"0,1\" #>  #> $MC$covariate_generation$types$cont_random$tooltip #> [1] \"Sample randomly from within the specified range.\" #>  #>  #> $MC$covariate_generation$types$cont_normal #> $MC$covariate_generation$types$cont_normal$choice #> [1] \"Sample normally\" #>  #> $MC$covariate_generation$types$cont_normal$type #> [1] \"continuous\" #>  #> $MC$covariate_generation$types$cont_normal$sampling #> [1] \"normal\" #>  #> $MC$covariate_generation$types$cont_normal$placeholder #> [1] \"mean, variance\" #>  #> $MC$covariate_generation$types$cont_normal$tooltip #> [1] \"Sample from normal distribution provided a mean and variance\" #>  #>  #> $MC$covariate_generation$types$cont_lognormal #> $MC$covariate_generation$types$cont_lognormal$choice #> [1] \"Sample log-normally\" #>  #> $MC$covariate_generation$types$cont_lognormal$type #> [1] \"continuous\" #>  #> $MC$covariate_generation$types$cont_lognormal$sampling #> [1] \"log-normal\" #>  #> $MC$covariate_generation$types$cont_lognormal$placeholder #> [1] \"mean, variance\" #>  #> $MC$covariate_generation$types$cont_lognormal$tooltip #> [1] \"Sample from log-normal distribution provided a mean and variance\" #>  #>  #>  #>  #>  #> $CTS #> $CTS$button_counters #> $CTS$button_counters$button_clk_runsim #> [1] 0 #>  #> $CTS$button_counters$button_clk_update_plot #> [1] 0 #>  #> $CTS$button_counters$button_clk_save #> [1] 0 #>  #> $CTS$button_counters$button_clk_clip #> [1] 0 #>  #> $CTS$button_counters$button_clk_del #> [1] 0 #>  #> $CTS$button_counters$button_clk_copy #> [1] 0 #>  #> $CTS$button_counters$button_clk_new #> [1] 0 #>  #> $CTS$button_counters$button_clk_add_cov #> [1] 0 #>  #> $CTS$button_counters$button_clk_add_rule #> [1] 0 #>  #>  #> $CTS$ui_hold #> $CTS$ui_hold$button_clk_runsim #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_update_plot #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_save #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_clip #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_del #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_copy #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_new #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_add_cov #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_add_rule #> [1] FALSE #>  #> $CTS$ui_hold$element_name #> [1] FALSE #>  #> $CTS$ui_hold$nsub #> [1] FALSE #>  #> $CTS$ui_hold$fpage #> [1] FALSE #>  #> $CTS$ui_hold$dvcols #> [1] FALSE #>  #> $CTS$ui_hold$tc_dim #> [1] FALSE #>  #> $CTS$ui_hold$evplot #> [1] FALSE #>  #> $CTS$ui_hold$visit_times #> [1] FALSE #>  #> $CTS$ui_hold$trial_end #> [1] FALSE #>  #> $CTS$ui_hold$rule_condition #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_state #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_values #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_times #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_durations #> [1] FALSE #>  #> $CTS$ui_hold$action_set_state_state #> [1] FALSE #>  #> $CTS$ui_hold$action_set_state_value #> [1] FALSE #>  #> $CTS$ui_hold$action_manual_code #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_atol #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_rtol #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_sigdig #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_seed #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_nsteps #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_preamble #> [1] FALSE #>  #> $CTS$ui_hold$rule_type #> [1] FALSE #>  #> $CTS$ui_hold$rule_name #> [1] FALSE #>  #> $CTS$ui_hold$source_model #> [1] FALSE #>  #> $CTS$ui_hold$hot_current_rules #> [1] FALSE #>  #> $CTS$ui_hold$covariate_type_selected #> [1] FALSE #>  #> $CTS$ui_hold$covariate_value #> [1] FALSE #>  #> $CTS$ui_hold$selected_covariate #> [1] FALSE #>  #> $CTS$ui_hold$switch_output #> [1] FALSE #>  #> $CTS$ui_hold$element_selection #> [1] FALSE #>  #>  #> $CTS$ui_ids #>  [1] \"button_clk_runsim\"       \"button_clk_update_plot\"  #>  [3] \"button_clk_save\"         \"button_clk_clip\"         #>  [5] \"button_clk_del\"          \"button_clk_copy\"         #>  [7] \"button_clk_new\"          \"button_clk_add_cov\"      #>  [9] \"button_clk_add_rule\"     \"element_name\"            #> [11] \"nsub\"                    \"fpage\"                   #> [13] \"dvcols\"                  \"tc_dim\"                  #> [15] \"evplot\"                  \"visit_times\"             #> [17] \"trial_end\"               \"rule_condition\"          #> [19] \"action_dosing_state\"     \"action_dosing_values\"    #> [21] \"action_dosing_times\"     \"action_dosing_durations\" #> [23] \"action_set_state_state\"  \"action_set_state_value\"  #> [25] \"action_manual_code\"      \"cts_config_atol\"         #> [27] \"cts_config_rtol\"         \"cts_config_sigdig\"       #> [29] \"cts_config_seed\"         \"cts_config_nsteps\"       #> [31] \"cts_config_preamble\"     \"rule_type\"               #> [33] \"rule_name\"               \"source_model\"            #> [35] \"hot_current_rules\"       \"covariate_type_selected\" #> [37] \"covariate_value\"         \"selected_covariate\"      #> [39] \"switch_output\"           \"element_selection\"       #>  #> $CTS$isgood #> [1] TRUE #>  #> $CTS$MDL #> $CTS$MDL$isgood #> [1] TRUE #>  #> $CTS$MDL$hasmdl #> [1] TRUE #>  #> $CTS$MDL$catalog #>                   label      object MOD_TYPE id #> 1 One compartment model MB_obj_1_rx       MB MB #> 2          PK Biomarker MB_obj_2_rx       MB MB #>                           checksum                      MDLchecksum #> 1 bc8090bd2fe05a30d8e0d5b5ef604e24 ef47c330d3ab2d804b6cec26d53b9683 #> 2 bc8090bd2fe05a30d8e0d5b5ef604e24 8fe63af71c1f0c6b582646ec3c4c31b0 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          MB_obj_1_fcn = function () \\n{\\n    description <- \"One compartment PK model with linear clearance using differential equations\"\\n    ini({\\n        lka <- 0.45\\n        label(\"Absorption rate (Ka)\")\\n        lcl <- 1\\n        label(\"Clearance (CL)\")\\n        lvc <- 3.45\\n        label(\"Central volume of distribution (V)\")\\n        propSd <- c(0, 0.5)\\n        label(\"Proportional residual error (fraction)\")\\n        etalcl ~ 0.1\\n    })\\n    model({\\n        ka <- exp(lka)\\n        cl <- exp(lcl + etalcl)\\n        vc <- exp(lvc)\\n        kel <- cl/vc\\n        d/dt(depot) <- -ka * depot\\n        d/dt(central) <- ka * depot - kel * central\\n        Cc <- central/vc\\n        Cc ~ prop(propSd)\\n    })\\n}\\nMB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #> 2 MB_obj_2_fcn = function () \\n{\\n    ini({\\n        TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf)\\n        TV_ka <- c(2.22044604925031e-16, 0.282, Inf)\\n        TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\\n        TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\\n        TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\\n        TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\\n        TV_MW <- fix(2.22044604925031e-16, 140, Inf)\\n        TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf)\\n        TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf)\\n        TV_Emax <- c(2.22044604925031e-16, 100, Inf)\\n        TV_EC50 <- fix(2.22044604925031e-16, 300, Inf)\\n        prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_BM <- c(2.22044604925031e-16, 0.1, Inf)\\n        ETAka ~ 0.416\\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \\n            0.0377, 0.0789)\\n        ETAQ ~ 0.699\\n        ETABM_IC ~ 0.05\\n        ETAEmax ~ 0.1\\n    })\\n    model({\\n        F1 = TV_F1\\n        ka = TV_ka * exp(ETAka)\\n        CL = exp(TV_CL) * exp(ETACL)\\n        Vc = exp(TV_Vc) * exp(ETAVc)\\n        Cc = Ac/(Vc)\\n        Vp = exp(TV_Vp) * exp(ETAVp)\\n        Q = exp(TV_Q) * exp(ETAQ)\\n        MW = TV_MW\\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\\n        kdeg_BM = TV_kdeg_BM\\n        Emax = TV_Emax * exp(ETAEmax)\\n        EC50 = TV_EC50\\n        Dinf = 0\\n        WTTV = 70\\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \\n            0.1)\\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\\n        ksyn_BM = kdeg_BM * BM_IC\\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\\n        BM(0) = BM_IC\\n        d/dt(At) = (-ka * At)\\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \\n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\\n        C_ng_ml = Cc * MW\\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\\n        BM_obs = BM\\n        BM_obs ~ add(add_err_BM)\\n    })\\n}\\nMB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>  #> $CTS$MDL$modules #> $CTS$MDL$modules$MB #> $CTS$MDL$modules$MB$MB #> [1] \"bc8090bd2fe05a30d8e0d5b5ef604e24\" #>  #>  #>  #> $CTS$MDL$mdl #> $CTS$MDL$mdl$MB_obj_1_rx #> $CTS$MDL$mdl$MB_obj_1_rx$label #> [1] \"One compartment model\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$MOD_TYPE #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$id #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$rx_obj #>  ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>    lka    lcl    lvc propSd  #>   0.45   1.00   3.45   0.50  #>  #> Omega ($omega):  #>        etalcl #> etalcl    0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1            depot #> 2                  2          central #>  ── μ-referencing ($muRefTable): ──   #>   theta    eta level #> 1   lcl etalcl    id #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     description <- \"One compartment PK model with linear clearance using differential equations\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>         etalcl ~ 0.1 #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl + etalcl) #>         vc <- exp(lvc) #>         kel <- cl/vc #>         d/dt(depot) <- -ka * depot #>         d/dt(central) <- ka * depot - kel * central #>         Cc <- central/vc #>         Cc ~ prop(propSd) #>     }) #> } #>  #> $CTS$MDL$mdl$MB_obj_1_rx$rx_obj_name #> [1] \"MB_obj_1_rx\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$fcn_def #> [1] \"function () \\n{\\n    description <- \\\"One compartment PK model with linear clearance using differential equations\\\"\\n    ini({\\n        lka <- 0.45\\n        label(\\\"Absorption rate (Ka)\\\")\\n        lcl <- 1\\n        label(\\\"Clearance (CL)\\\")\\n        lvc <- 3.45\\n        label(\\\"Central volume of distribution (V)\\\")\\n        propSd <- c(0, 0.5)\\n        label(\\\"Proportional residual error (fraction)\\\")\\n        etalcl ~ 0.1\\n    })\\n    model({\\n        ka <- exp(lka)\\n        cl <- exp(lcl + etalcl)\\n        vc <- exp(lvc)\\n        kel <- cl/vc\\n        d/dt(depot) <- -ka * depot\\n        d/dt(central) <- ka * depot - kel * central\\n        Cc <- central/vc\\n        Cc ~ prop(propSd)\\n    })\\n}\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$MDLMETA #> [1] \"base model\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$code #> [1] \"MB_obj_1_fcn = function () \\n{\\n    description <- \\\"One compartment PK model with linear clearance using differential equations\\\"\\n    ini({\\n        lka <- 0.45\\n        label(\\\"Absorption rate (Ka)\\\")\\n        lcl <- 1\\n        label(\\\"Clearance (CL)\\\")\\n        lvc <- 3.45\\n        label(\\\"Central volume of distribution (V)\\\")\\n        propSd <- c(0, 0.5)\\n        label(\\\"Proportional residual error (fraction)\\\")\\n        etalcl ~ 0.1\\n    })\\n    model({\\n        ka <- exp(lka)\\n        cl <- exp(lcl + etalcl)\\n        vc <- exp(lvc)\\n        kel <- cl/vc\\n        d/dt(depot) <- -ka * depot\\n        d/dt(central) <- ka * depot - kel * central\\n        Cc <- central/vc\\n        Cc ~ prop(propSd)\\n    })\\n}\\nMB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn)\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$checksum #> [1] \"bc8090bd2fe05a30d8e0d5b5ef604e24\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$MDLchecksum #> [1] \"ef47c330d3ab2d804b6cec26d53b9683\" #>  #>  #> $CTS$MDL$mdl$MB_obj_2_rx #> $CTS$MDL$mdl$MB_obj_2_rx$label #> [1] \"PK Biomarker\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$MOD_TYPE #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$id #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$rx_obj #>  ── rxode2-based free-form 4-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>        TV_F1        TV_ka        TV_CL        TV_Vc        TV_Vp         TV_Q  #>    0.7440000    0.2820000   -1.6094379    1.2837078    1.0116009   -0.2916901  #>        TV_MW     TV_BM_IC   TV_kdeg_BM      TV_Emax      TV_EC50  prop_err_PK  #>  140.0000000 1000.0000000    0.1000000  100.0000000  300.0000000    0.1000000  #>   add_err_PK   add_err_BM  #>    0.1000000    0.1000000  #>  #> Omega ($omega):  #>          ETAka   ETACL  ETAVc  ETAVp  ETAQ ETABM_IC ETAEmax #> ETAka    0.416 0.00000 0.0000 0.0000 0.000     0.00     0.0 #> ETACL    0.000 0.09875 0.0786 0.0619 0.000     0.00     0.0 #> ETAVc    0.000 0.07860 0.1160 0.0377 0.000     0.00     0.0 #> ETAVp    0.000 0.06190 0.0377 0.0789 0.000     0.00     0.0 #> ETAQ     0.000 0.00000 0.0000 0.0000 0.699     0.00     0.0 #> ETABM_IC 0.000 0.00000 0.0000 0.0000 0.000     0.05     0.0 #> ETAEmax  0.000 0.00000 0.0000 0.0000 0.000     0.00     0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1               At #> 2                  2               Ac #> 3                  3               Cp #> 4                  4               BM #>  ── Multiple Endpoint Model ($multipleEndpoint): ──   #>      variable                    cmt                    dvid* #> 1 C_ng_ml ~ … cmt='C_ng_ml' or cmt=5 dvid='C_ng_ml' or dvid=1 #> 2  BM_obs ~ …  cmt='BM_obs' or cmt=6  dvid='BM_obs' or dvid=2 #>   * If dvids are outside this range, all dvids are re-numered sequentially, ie 1,7, 10 becomes 1,2,3 etc #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #>  #> $CTS$MDL$mdl$MB_obj_2_rx$rx_obj_name #> [1] \"MB_obj_2_rx\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$fcn_def #> [1] \"function () \\n{\\n    ini({\\n        TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf)\\n        TV_ka <- c(2.22044604925031e-16, 0.282, Inf)\\n        TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\\n        TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\\n        TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\\n        TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\\n        TV_MW <- fix(2.22044604925031e-16, 140, Inf)\\n        TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf)\\n        TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf)\\n        TV_Emax <- c(2.22044604925031e-16, 100, Inf)\\n        TV_EC50 <- fix(2.22044604925031e-16, 300, Inf)\\n        prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_BM <- c(2.22044604925031e-16, 0.1, Inf)\\n        ETAka ~ 0.416\\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \\n            0.0377, 0.0789)\\n        ETAQ ~ 0.699\\n        ETABM_IC ~ 0.05\\n        ETAEmax ~ 0.1\\n    })\\n    model({\\n        F1 = TV_F1\\n        ka = TV_ka * exp(ETAka)\\n        CL = exp(TV_CL) * exp(ETACL)\\n        Vc = exp(TV_Vc) * exp(ETAVc)\\n        Cc = Ac/(Vc)\\n        Vp = exp(TV_Vp) * exp(ETAVp)\\n        Q = exp(TV_Q) * exp(ETAQ)\\n        MW = TV_MW\\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\\n        kdeg_BM = TV_kdeg_BM\\n        Emax = TV_Emax * exp(ETAEmax)\\n        EC50 = TV_EC50\\n        Dinf = 0\\n        WTTV = 70\\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \\n            0.1)\\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\\n        ksyn_BM = kdeg_BM * BM_IC\\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\\n        BM(0) = BM_IC\\n        d/dt(At) = (-ka * At)\\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \\n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\\n        C_ng_ml = Cc * MW\\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\\n        BM_obs = BM\\n        BM_obs ~ add(add_err_BM)\\n    })\\n}\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$MDLMETA #> [1] \"base model\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$code #> [1] \"MB_obj_2_fcn = function () \\n{\\n    ini({\\n        TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf)\\n        TV_ka <- c(2.22044604925031e-16, 0.282, Inf)\\n        TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\\n        TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\\n        TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\\n        TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\\n        TV_MW <- fix(2.22044604925031e-16, 140, Inf)\\n        TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf)\\n        TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf)\\n        TV_Emax <- c(2.22044604925031e-16, 100, Inf)\\n        TV_EC50 <- fix(2.22044604925031e-16, 300, Inf)\\n        prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_BM <- c(2.22044604925031e-16, 0.1, Inf)\\n        ETAka ~ 0.416\\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \\n            0.0377, 0.0789)\\n        ETAQ ~ 0.699\\n        ETABM_IC ~ 0.05\\n        ETAEmax ~ 0.1\\n    })\\n    model({\\n        F1 = TV_F1\\n        ka = TV_ka * exp(ETAka)\\n        CL = exp(TV_CL) * exp(ETACL)\\n        Vc = exp(TV_Vc) * exp(ETAVc)\\n        Cc = Ac/(Vc)\\n        Vp = exp(TV_Vp) * exp(ETAVp)\\n        Q = exp(TV_Q) * exp(ETAQ)\\n        MW = TV_MW\\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\\n        kdeg_BM = TV_kdeg_BM\\n        Emax = TV_Emax * exp(ETAEmax)\\n        EC50 = TV_EC50\\n        Dinf = 0\\n        WTTV = 70\\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \\n            0.1)\\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\\n        ksyn_BM = kdeg_BM * BM_IC\\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\\n        BM(0) = BM_IC\\n        d/dt(At) = (-ka * At)\\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \\n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\\n        C_ng_ml = Cc * MW\\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\\n        BM_obs = BM\\n        BM_obs ~ add(add_err_BM)\\n    })\\n}\\nMB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn)\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$checksum #> [1] \"bc8090bd2fe05a30d8e0d5b5ef604e24\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$MDLchecksum #> [1] \"8fe63af71c1f0c6b582646ec3c4c31b0\" #>  #>  #>  #>  #> $CTS$ui_ele #>  [1] \"element_name\"            \"nsub\"                    #>  [3] \"fpage\"                   \"dvcols\"                  #>  [5] \"tc_dim\"                  \"evplot\"                  #>  [7] \"visit_times\"             \"trial_end\"               #>  [9] \"rule_condition\"          \"action_dosing_state\"     #> [11] \"action_dosing_values\"    \"action_dosing_times\"     #> [13] \"action_dosing_durations\" \"action_set_state_state\"  #> [15] \"action_set_state_value\"  \"action_manual_code\"      #> [17] \"cts_config_atol\"         \"cts_config_rtol\"         #> [19] \"cts_config_sigdig\"       \"cts_config_seed\"         #> [21] \"cts_config_nsteps\"       \"cts_config_preamble\"     #>  #> $CTS$element_cntr #> [1] 1 #>  #> $CTS$sc_meta #> $CTS$sc_meta$config #> $CTS$sc_meta$config$atol #> $CTS$sc_meta$config$atol$group #> [1] \"ODE Solver\" #>  #> $CTS$sc_meta$config$atol$label #> [1] \"Absolute tolerance\" #>  #> $CTS$sc_meta$config$atol$tooltip #> [1] \"Absolute tolerance used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $CTS$sc_meta$config$atol$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$atol$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$atol$value #> [1] \"1e-8\" #>  #> $CTS$sc_meta$config$atol$options #> NULL #>  #> $CTS$sc_meta$config$atol$use #> [1] \"rxSolve\" #>  #> $CTS$sc_meta$config$atol$placeholder #> [1] \"1e-8\" #>  #> $CTS$sc_meta$config$atol$ui #> [1] \"cts_config_atol\" #>  #>  #> $CTS$sc_meta$config$rtol #> $CTS$sc_meta$config$rtol$group #> [1] \"ODE Solver\" #>  #> $CTS$sc_meta$config$rtol$label #> [1] \"Relative tolerance\" #>  #> $CTS$sc_meta$config$rtol$tooltip #> [1] \"Relative tolerance used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $CTS$sc_meta$config$rtol$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$rtol$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$rtol$value #> [1] \"1e-6\" #>  #> $CTS$sc_meta$config$rtol$options #> NULL #>  #> $CTS$sc_meta$config$rtol$use #> [1] \"rxSolve\" #>  #> $CTS$sc_meta$config$rtol$placeholder #> [1] \"1e-6\" #>  #> $CTS$sc_meta$config$rtol$ui #> [1] \"cts_config_rtol\" #>  #>  #> $CTS$sc_meta$config$sigdig #> $CTS$sc_meta$config$sigdig$group #> [1] \"ODE Solver\" #>  #> $CTS$sc_meta$config$sigdig$label #> [1] \"Significant digits\" #>  #> $CTS$sc_meta$config$sigdig$tooltip #> [1] \"Specifies the \\\"significant digits\\\" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\\\\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\\\\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\\\\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.\" #>  #> $CTS$sc_meta$config$sigdig$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$sigdig$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$sigdig$value #> [1] \"NULL\" #>  #> $CTS$sc_meta$config$sigdig$options #> NULL #>  #> $CTS$sc_meta$config$sigdig$use #> [1] \"rxSolve\" #>  #> $CTS$sc_meta$config$sigdig$placeholder #> [1] \"NULL\" #>  #> $CTS$sc_meta$config$sigdig$ui #> [1] \"cts_config_sigdig\" #>  #>  #> $CTS$sc_meta$config$seed #> $CTS$sc_meta$config$seed$group #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$seed$label #> [1] \"Random Seed\" #>  #> $CTS$sc_meta$config$seed$tooltip #> [1] \"Integer value to set the seed for the random number generator\" #>  #> $CTS$sc_meta$config$seed$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$seed$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$seed$value #> [1] \"86753090\" #>  #> $CTS$sc_meta$config$seed$options #> NULL #>  #> $CTS$sc_meta$config$seed$use #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$seed$placeholder #> [1] \"8675309\" #>  #> $CTS$sc_meta$config$seed$ui #> [1] \"cts_config_seed\" #>  #>  #> $CTS$sc_meta$config$nsteps #> $CTS$sc_meta$config$nsteps$group #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$nsteps$label #> [1] \"Time Steps\" #>  #> $CTS$sc_meta$config$nsteps$tooltip #> [1] \"Integer value representing the number of time steps to add between zero and the end of trial. A larger value will give smoother curves but it will also take more time to simulate.\" #>  #> $CTS$sc_meta$config$nsteps$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$nsteps$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$nsteps$value #> [1] \"200\" #>  #> $CTS$sc_meta$config$nsteps$options #> NULL #>  #> $CTS$sc_meta$config$nsteps$use #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$nsteps$placeholder #> [1] \"200\" #>  #> $CTS$sc_meta$config$nsteps$ui #> [1] \"cts_config_nsteps\" #>  #>  #> $CTS$sc_meta$config$preamble #> $CTS$sc_meta$config$preamble$group #> [1] \"Rule Environment Preamble\" #>  #> $CTS$sc_meta$config$preamble$label #> NULL #>  #> $CTS$sc_meta$config$preamble$tooltip #> [1] \"Optional user-defined code to be executed in the rule-evaluation environment. You can create user-defined functions here.\" #>  #> $CTS$sc_meta$config$preamble$uitype #> [1] \"textarea\" #>  #> $CTS$sc_meta$config$preamble$type #> [1] \"character\" #>  #> $CTS$sc_meta$config$preamble$value #> NULL #>  #> $CTS$sc_meta$config$preamble$options #> NULL #>  #> $CTS$sc_meta$config$preamble$use #> [1] \"rules\" #>  #> $CTS$sc_meta$config$preamble$placeholder #> [1] \"my_func = function(id=NULL){\\n\\n}\" #>  #> $CTS$sc_meta$config$preamble$ui #> [1] \"cts_config_preamble\" #>  #>  #>  #> $CTS$sc_meta$cfg_summary #>       name                  ui     use      type                     group #> 1     atol     cts_config_atol rxSolve   numeric                ODE Solver #> 2     rtol     cts_config_rtol rxSolve   numeric                ODE Solver #> 3   sigdig   cts_config_sigdig rxSolve   numeric                ODE Solver #> 4     seed     cts_config_seed    Misc   numeric                      Misc #> 5   nsteps   cts_config_nsteps    Misc   numeric                      Misc #> 6 preamble cts_config_preamble   rules character Rule Environment Preamble #>  #> $CTS$sc_meta$ui_config #> [1] \"cts_config_atol\"     \"cts_config_rtol\"     \"cts_config_sigdig\"   #> [4] \"cts_config_seed\"     \"cts_config_nsteps\"   \"cts_config_preamble\" #>  #>  #> $CTS$elements #> $CTS$elements$element_1 #> $CTS$elements$element_1$isgood #> [1] TRUE #>  #> $CTS$elements$element_1$ui #> $CTS$elements$element_1$ui$element_name #> [1] \"cohort 1\" #>  #> $CTS$elements$element_1$ui$nsub #> [1] 10 #>  #> $CTS$elements$element_1$ui$visit_times #> [1] 0 #>  #> $CTS$elements$element_1$ui$trial_end #> [1] 100 #>  #> $CTS$elements$element_1$ui$fpage #> [1] \"1\" #>  #> $CTS$elements$element_1$ui$dvcols #> [1] \"\" #>  #> $CTS$elements$element_1$ui$tc_dim #> [1] \"d2x4\" #>  #> $CTS$elements$element_1$ui$evplot #> [1] 1 #>  #> $CTS$elements$element_1$ui$source_model #> [1] \"MB_obj_1_rx\" #>  #> $CTS$elements$element_1$ui$cts_config_atol #> [1] \"1e-8\" #>  #> $CTS$elements$element_1$ui$cts_config_rtol #> [1] \"1e-6\" #>  #> $CTS$elements$element_1$ui$cts_config_sigdig #> [1] \"NULL\" #>  #> $CTS$elements$element_1$ui$cts_config_seed #> [1] \"86753090\" #>  #> $CTS$elements$element_1$ui$cts_config_nsteps #> [1] \"200\" #>  #>  #> $CTS$elements$element_1$id #> [1] \"element_1\" #>  #> $CTS$elements$element_1$idx #> [1] 1 #>  #> $CTS$elements$element_1$element_object_name #> [1] \"CTS_obj_1\" #>  #> $CTS$elements$element_1$cov_object_name #> [1] \"CTS_obj_1_cov\" #>  #> $CTS$elements$element_1$rules_object_name #> [1] \"CTS_obj_1_rules\" #>  #> $CTS$elements$element_1$subs_object_name #> [1] \"CTS_obj_1_subs\" #>  #> $CTS$elements$element_1$rxopts_object_name #> [1] \"CTS_obj_1_rxopts\" #>  #> $CTS$elements$element_1$simres_object_name #> [1] \"CTS_obj_1_simres\" #>  #> $CTS$elements$element_1$ot_object_name #> [1] \"CTS_obj_1_output_times\" #>  #> $CTS$elements$element_1$fgtc_object_name #> [1] \"CTS_obj_1_fgtc\" #>  #> $CTS$elements$element_1$fgev_object_name #> [1] \"CTS_obj_1_fgev\" #>  #> $CTS$elements$element_1$code_previous #> [1] \"\" #>  #> $CTS$elements$element_1$rx_details #> $CTS$elements$element_1$rx_details$isgood #> [1] TRUE #>  #> $CTS$elements$element_1$rx_details$msgs #> NULL #>  #> $CTS$elements$element_1$rx_details$elements #> $CTS$elements$element_1$rx_details$elements$covariates #> character(0) #>  #> $CTS$elements$element_1$rx_details$elements$population #> [1] \"lka\" \"lcl\" \"lvc\" #>  #> $CTS$elements$element_1$rx_details$elements$parameters #> [1] \"ka\" \"cl\" \"vc\" #>  #> $CTS$elements$element_1$rx_details$elements$secondary #> [1] \"kel\" #>  #> $CTS$elements$element_1$rx_details$elements$residual_error #> [1] \"propSd\" #>  #> $CTS$elements$element_1$rx_details$elements$iiv #> [1] \"etalcl\" #>  #> $CTS$elements$element_1$rx_details$elements$outputs #> [1] \"Cc\" #>  #> $CTS$elements$element_1$rx_details$elements$states #> [1] \"depot\"   \"central\" #>  #>  #> $CTS$elements$element_1$rx_details$txt_info #> [1] \"Outputs\\nCc\\n\\nStates/Compartments\\ndepot, central\\n\\nCovariates\\nNone found\\n\\nPopulation Parameters\\nlka, lcl, lvc\\n\\nIndividual Parameters\\nka, cl, vc\\n\\nSecondary Parameters\\nkel\\n\\nBetween-Subject Variability\\netalcl\\n\\nResidual Error Parameters\\npropSd\\n\\n\" #>  #> $CTS$elements$element_1$rx_details$ht_info #> <b>Outputs<\/b> #> <br/> #> Cc #> <br/> #> <br/> #> <b>States/Compartments<\/b> #> <br/> #> depot, central #> <br/> #> <br/> #> <b>Covariates<\/b> #> <br/> #> <em>None found<\/em> #> <br/> #> <br/> #> <b>Population Parameters<\/b> #> <br/> #> lka, lcl, lvc #> <br/> #> <br/> #> <b>Individual Parameters<\/b> #> <br/> #> ka, cl, vc #> <br/> #> <br/> #> <b>Secondary Parameters<\/b> #> <br/> #> kel #> <br/> #> <br/> #> <b>Between-Subject Variability<\/b> #> <br/> #> etalcl #> <br/> #> <br/> #> <b>Residual Error Parameters<\/b> #> <br/> #> propSd #> <br/> #> <br/> #>  #>  #> $CTS$elements$element_1$components_table #> NULL #>  #> $CTS$elements$element_1$components_list #> list() #>  #> $CTS$elements$element_1$covariates #> list() #>  #> $CTS$elements$element_1$checksum #> [1] \"309d9f0f0e7937e139ada00751bc8f2a\" #>  #> $CTS$elements$element_1$code #> NULL #>  #> $CTS$elements$element_1$code_ele_only #> NULL #>  #>  #>  #> $CTS$current_element #> [1] \"element_1\" #>  #> $CTS$checksum #> [1] \"4affbc8f5039c017fb6d555c8aa0f450\" #>  #>  #> $MOD_TYPE #> [1] \"CTS\" #>  #> $id #> [1] \"CTS\" #>  #> $dep_mod_ids #> [1] \"MB\" #>  #> $FM_yaml_file #> [1] \"/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/formods/templates/formods.yaml\" #>  #> $MOD_yaml_file #> [1] \"/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/RtmpYagqQE/temp_libpathc4691c5677bf/ruminate/templates/CTS.yaml\" #>  #> $shiny_token #> [1] \"non_shiny\" #>"},{"path":"/reference/CTS_new_element.html","id":null,"dir":"Reference","previous_headings":"","what":"New ===Module_Name=== cohort — CTS_new_element","title":"New ===Module_Name=== cohort — CTS_new_element","text":"Appends new empty cohort CTS state object makes new cohort active cohort.","code":""},{"path":"/reference/CTS_new_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"New ===Module_Name=== cohort — CTS_new_element","text":"","code":"CTS_new_element(state)"},{"path":"/reference/CTS_new_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"New ===Module_Name=== cohort — CTS_new_element","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_new_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"New ===Module_Name=== cohort — CTS_new_element","text":"CTS state object containing new cohort cohort set current active cohort. See help CTS_fetch_state() ===ELEMENT== format.","code":""},{"path":"/reference/CTS_new_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"New ===Module_Name=== cohort — CTS_new_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_plot_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the Specified Element — CTS_plot_element","title":"Plots the Specified Element — CTS_plot_element","text":"Takes CTS state element simulates current set rules.","code":""},{"path":"/reference/CTS_plot_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the Specified Element — CTS_plot_element","text":"","code":"CTS_plot_element(state, element)"},{"path":"/reference/CTS_plot_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the Specified Element — CTS_plot_element","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_plot_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the Specified Element — CTS_plot_element","text":"Simulation element plot results stored '\"plotres\" element. isgood   Boolean value indicating state figure generation code. msgs     messages passed user. capture  Captured figure generation output plot_sr_tc()","code":""},{"path":"/reference/CTS_plot_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots the Specified Element — CTS_plot_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_set_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the Value for the  Current cohort — CTS_set_current_element","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"Takes CTS state returns current active cohort","code":""},{"path":"/reference/CTS_set_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"","code":"CTS_set_current_element(state, element)"},{"path":"/reference/CTS_set_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_set_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"CTS state object current cohort set using supplied value.","code":""},{"path":"/reference/CTS_set_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_simulate_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulates the Specified Element — CTS_simulate_element","title":"Simulates the Specified Element — CTS_simulate_element","text":"Takes CTS state element simulates current set rules.","code":""},{"path":"/reference/CTS_simulate_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulates the Specified Element — CTS_simulate_element","text":"","code":"CTS_simulate_element(state, element)"},{"path":"/reference/CTS_simulate_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulates the Specified Element — CTS_simulate_element","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_simulate_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulates the Specified Element — CTS_simulate_element","text":"Simulation element simulation results stored \"simres\" element.","code":""},{"path":"/reference/CTS_simulate_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulates the Specified Element — CTS_simulate_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/CTS_test_mksession.html","id":null,"dir":"Reference","previous_headings":"","what":"Populate Session Data for Module Testing — CTS_test_mksession","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"Populates supplied session variable testing.","code":""},{"path":"/reference/CTS_test_mksession.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"","code":"CTS_test_mksession(session, id = \"CTS\", id_MB = \"MB\", full_session = TRUE)"},{"path":"/reference/CTS_test_mksession.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"session Shiny session variable (app) list (outside app) id ID string corresponds ID used call modules UI elements id_MB ID string corresponds ID used call MB modules full_session Boolean indicate full test session created (default TRUE).","code":""},{"path":"/reference/CTS_test_mksession.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"list following elements isgood: Boolean indicating exit status function. session: value Shiny session variable (app) list (outside app) initialization. input: value shiny input end session initialization. state: App state. rsc: react_state components.","code":""},{"path":"/reference/CTS_test_mksession.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"","code":"sess_res = CTS_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: d65fc1e16b1d446683f2142b97a089b3 #> → CTS: module checksum updated: d674915b7cbf0a92210af8616420f77b #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: ee602f70b4927bab41a53fad3eca365f #> → CTS: module checksum updated: 9afe9ab401a8d39ef5cf5ba5cb74078d #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 85ee0d788dba2ed1c88ef6d5331f13b7 #> → CTS: module checksum updated: 5d2ddd21fdfe5ad2b24ddbc7b6bd28a5"},{"path":"/reference/CTS_update_checksum.html","id":null,"dir":"Reference","previous_headings":"","what":"Updates CTS Module Checksum — CTS_update_checksum","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"Takes CTS state updates checksum used trigger downstream updates","code":""},{"path":"/reference/CTS_update_checksum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"","code":"CTS_update_checksum(state)"},{"path":"/reference/CTS_update_checksum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_update_checksum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"CTS state object checksum updated","code":""},{"path":"/reference/CTS_update_checksum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> Error in is_installed(\"rxode2\"): could not find function \"is_installed\""},{"path":"/reference/FM_fetch_mdl.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Models from Modules in the App — FM_fetch_mdl","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"Loops specified module ID modules ID specified. ID, attempt made extract models available.","code":""},{"path":"/reference/FM_fetch_mdl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"","code":"FM_fetch_mdl(state, session, ids = NULL)"},{"path":"/reference/FM_fetch_mdl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"state Current module state yaml file read session Shiny session variable ids Vector ID strings modules containing models NULL modules models available.","code":""},{"path":"/reference/FM_fetch_mdl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"list containing current dataset following format: JMH isgood: General logical indicator successfully. hasmdl: Logical indicating least one model found. modules: List module checksums. mdl: Result XX_fetch_mdl, see  vignette(\"making_modules\", package = \"formods\") catalog: Dataframe containing tabular catalog models found. label:  Text label model. object :  Name object contains compiled rxode2 model. MOD_TYPE:  Type 'formods' module model came . id: Source 'formods' Module ID. checksum: Checksum module model came . MDLchecksum: Checksum model. code:  Code generate model.","code":""},{"path":"/reference/FM_fetch_mdl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"","code":"# We need a module state and a Shiny session variable # to use this function: id=\"UD\" sess_res = UD_test_mksession(session=list(), id=id) #> Error in UD_test_mksession(session = list(), id = id): could not find function \"UD_test_mksession\" session = sess_res$session #> Error in eval(expr, envir, enclos): object 'sess_res' not found state   = sess_res$state #> Error in eval(expr, envir, enclos): object 'sess_res' not found mdl = FM_fetch_mdl(state, session) #> Error in FM_fetch_app_state(session): object 'session' not found mdl$catalog #> Error in eval(expr, envir, enclos): object 'mdl' not found"},{"path":"/reference/MB_Server.html","id":null,"dir":"Reference","previous_headings":"","what":"Model Builder State Server — MB_Server","title":"Model Builder State Server — MB_Server","text":"Server function Model Builder  Shiny Module","code":""},{"path":"/reference/MB_Server.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Builder State Server — MB_Server","text":"","code":"MB_Server(   id,   id_ASM = \"ASM\",   FM_yaml_file = system.file(package = \"formods\", \"templates\", \"formods.yaml\"),   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\"),   deployed = FALSE,   react_state = NULL )"},{"path":"/reference/MB_Server.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Builder State Server — MB_Server","text":"id ID string corresponds ID used call modules UI elements id_ASM ID string app state managment module used save load app states FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. deployed Boolean variable indicating whether app deployed . react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/MB_Server.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model Builder State Server — MB_Server","text":"MB Server object","code":""},{"path":"/reference/MB_append_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Report Elements — MB_append_report","title":"Append Report Elements — MB_append_report","text":"Appends report elements formods report.","code":""},{"path":"/reference/MB_append_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Report Elements — MB_append_report","text":"","code":"MB_append_report(state, rpt, rpttype, gen_code_only = FALSE)"},{"path":"/reference/MB_append_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Report Elements — MB_append_report","text":"state MB state MB_fetch_state() rpt Report current content report appended function. details structure see documentation FM_generate_report rpttype Type report generate (supported \"xlsx\", \"pptx\", \"docx\"). gen_code_only Boolean value indicating code generated (FALSE).","code":""},{"path":"/reference/MB_append_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Report Elements — MB_append_report","text":"list containing following elements isgood:    Return status function. hasrptele: Boolean indicator module reportable elements. code:      Code generate reporting elements. msgs:      Messages passed back user. rpt:       Report additions passed back user.","code":""},{"path":[]},{"path":"/reference/MB_build_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Code to Generate Model — MB_build_code","title":"Build Code to Generate Model — MB_build_code","text":"Takes function definition rxode object, function object name rxode object name creates code build objects.","code":""},{"path":"/reference/MB_build_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Code to Generate Model — MB_build_code","text":"","code":"MB_build_code(state, session, fcn_def, fcn_obj_name, rx_obj_name)"},{"path":"/reference/MB_build_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Code to Generate Model — MB_build_code","text":"state MB state MB_fetch_state() session Shiny session variable fcn_def Character string containing function definition model fcn_obj_name Object name function create. rx_obj_name Object name rxode2 object create.","code":""},{"path":"/reference/MB_build_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Code to Generate Model — MB_build_code","text":"List following elements model_code Block code create model context larger script. model_code_sa model_code element meant stand alone.","code":""},{"path":"/reference/MB_build_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build Code to Generate Model — MB_build_code","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_del_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Deletes Current model — MB_del_current_element","title":"Deletes Current model — MB_del_current_element","text":"Takes MB state deletes current model. last element, new default added.","code":""},{"path":"/reference/MB_del_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deletes Current model — MB_del_current_element","text":"","code":"MB_del_current_element(state)"},{"path":"/reference/MB_del_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deletes Current model — MB_del_current_element","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_del_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deletes Current model — MB_del_current_element","text":"MB state object current model deleted.","code":""},{"path":"/reference/MB_del_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deletes Current model — MB_del_current_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_catalog.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches List of Available Models — MB_fetch_catalog","title":"Fetches List of Available Models — MB_fetch_catalog","text":"Creates catalog models available system file.","code":""},{"path":"/reference/MB_fetch_catalog.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches List of Available Models — MB_fetch_catalog","text":"","code":"MB_fetch_catalog(state)"},{"path":"/reference/MB_fetch_catalog.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches List of Available Models — MB_fetch_catalog","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_catalog.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches List of Available Models — MB_fetch_catalog","text":"List following attributes: summary: Dataframe summary models catlog sources: information found summary table list form. select_group: List models grouped source. select_plain: Flat list models (ungrouped). select_subtext: Subtext pulldown menus. msgs: Messages passed back user. hasmdl: Boolean value indicating models found. isgood: Boolean variable indicating success failure.","code":""},{"path":"/reference/MB_fetch_catalog.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches List of Available Models — MB_fetch_catalog","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Code — MB_fetch_code","title":"Fetch Module Code — MB_fetch_code","text":"Fetches code generate results seen app","code":""},{"path":"/reference/MB_fetch_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Code — MB_fetch_code","text":"","code":"MB_fetch_code(state)"},{"path":"/reference/MB_fetch_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Code — MB_fetch_code","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Code — MB_fetch_code","text":"Character object vector lines code","code":""},{"path":"/reference/MB_fetch_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Code — MB_fetch_code","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_component.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Selected Current Model Component — MB_fetch_component","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"Fetches selected component provided model.","code":""},{"path":"/reference/MB_fetch_component.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"","code":"MB_fetch_component(state, current_ele, component_id = NULL)"},{"path":"/reference/MB_fetch_component.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"state MB state MB_fetch_state() current_ele MB model element MB_fetch_current_element() component_id numeric component id select (default NULL) return selected ID.","code":""},{"path":"/reference/MB_fetch_component.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"list current component following attributes isgood: Boolean object indicating success. rx_obj: rxode2 object model. fcn_def: Just model function definition. note: Note field components_table model_code: Code generate model. model_code_sa: Stand-alone code generate model msgs:      Messages passed back user.","code":""},{"path":"/reference/MB_fetch_component.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Current model — MB_fetch_current_element","title":"Fetches Current model — MB_fetch_current_element","text":"Takes MB state returns current active model object.","code":""},{"path":"/reference/MB_fetch_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Current model — MB_fetch_current_element","text":"","code":"MB_fetch_current_element(state)"},{"path":"/reference/MB_fetch_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Current model — MB_fetch_current_element","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Current model — MB_fetch_current_element","text":"List containing details active data view. structure list structure state$MB$elements output MB_fetch_state().","code":""},{"path":"/reference/MB_fetch_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Current model — MB_fetch_current_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Model Builder Module Datasets — MB_fetch_ds","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"Fetches datasets contained module.","code":""},{"path":"/reference/MB_fetch_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"","code":"MB_fetch_ds(state)"},{"path":"/reference/MB_fetch_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"Character object vector lines code list containing following elements isgood:    Return status function. hasds:     Boolean indicator module datasets msgs:      Messages passed back user. ds:        List datasets. list element name R-object dataset. element following structure: label: Text label dataset MOD_TYPE: Short name type module. id: module ID DS: Dataframe containing actual dataset. DSMETA: Metadata describing DS code: Complete code build dataset. checksum: Module checksum. DSchecksum: Dataset checksum.","code":""},{"path":"/reference/MB_fetch_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"","code":"# We need a module state: sess_res = MB_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> → MB: State initialized state = sess_res$state  ds = MB_fetch_ds(state) #> Error in MB_fetch_ds(state): object 'object_name' not found  ds #> Error in eval(expr, envir, enclos): object 'ds' not found"},{"path":"/reference/MB_fetch_mdl.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Model Builder Module Models — MB_fetch_mdl","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"Fetches models contained module.","code":""},{"path":"/reference/MB_fetch_mdl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"","code":"MB_fetch_mdl(state)"},{"path":"/reference/MB_fetch_mdl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_mdl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"list containing following elements isgood:    Return status function. hasmdl:    Boolean indicator module models msgs:      Messages passed back user. mdl:       List models. list element name R-object dataset. element following structure: label:      Text label model (e.g. one-compartment model). MOD_TYPE:   Type module. id:         Module ID. rx_obj:     rxode2 object name holds model. fcn_def:    Text define model MDLMETA:    Notes model. code:       Code generate model. checksum:   Module checksum. MDLchecksum: Model checksum.","code":""},{"path":"/reference/MB_fetch_mdl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"","code":"# We need a module state: sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a state = sess_res$state  mdls = MB_fetch_mdl(state)  names(mdls) #> [1] \"hasmdl\" \"isgood\" \"msgs\"   \"mdl\""},{"path":"/reference/MB_fetch_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Model Builder State — MB_fetch_state","title":"Fetch Model Builder State — MB_fetch_state","text":"Merges default app options changes made UI","code":""},{"path":"/reference/MB_fetch_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Model Builder State — MB_fetch_state","text":"","code":"MB_fetch_state(   id,   id_ASM,   input,   session,   FM_yaml_file,   MOD_yaml_file,   react_state )"},{"path":"/reference/MB_fetch_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Model Builder State — MB_fetch_state","text":"id Shiny module ID id_ASM ID string app state management module used save load app states input Shiny input variable session Shiny session variable FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/MB_fetch_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Model Builder State — MB_fetch_state","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. MB: isgood: Boolean object indicating file successfully loaded. checksum: MD5 sum contents element can used detect changes state. MOD_TYPE: Character data containing type module \"MB\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/MB_fetch_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Model Builder State — MB_fetch_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        id_ASM          = \"ASM\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)"},{"path":"/reference/MB_init_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize MB Module State — MB_init_state","title":"Initialize MB Module State — MB_init_state","text":"Creates list initialized module state","code":""},{"path":"/reference/MB_init_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize MB Module State — MB_init_state","text":"","code":"MB_init_state(FM_yaml_file, MOD_yaml_file, id, session)"},{"path":"/reference/MB_init_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize MB Module State — MB_init_state","text":"FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id ID string module. session Shiny session variable","code":""},{"path":"/reference/MB_init_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize MB Module State — MB_init_state","text":"list containing empty MB state","code":""},{"path":"/reference/MB_init_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize MB Module State — MB_init_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a session = sess_res$session input   = sess_res$input  state = MB_init_state(    FM_yaml_file  = system.file(package = \"formods\",                                \"templates\",                                \"formods.yaml\"),    MOD_yaml_file = system.file(package = \"ruminate\",                                \"templates\",                                \"MB.yaml\"),    id              = \"MB\",    session         = session) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized  state #> $yaml #> $yaml$FM #> $yaml$FM$include #> $yaml$FM$include$files #> $yaml$FM$include$files[[1]] #> $yaml$FM$include$files[[1]]$file #> $yaml$FM$include$files[[1]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.docx\\\")\" #>  #> $yaml$FM$include$files[[1]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.docx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[2]] #> $yaml$FM$include$files[[2]]$file #> $yaml$FM$include$files[[2]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.pptx\\\")\" #>  #> $yaml$FM$include$files[[2]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.pptx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[3]] #> $yaml$FM$include$files[[3]]$file #> $yaml$FM$include$files[[3]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.yaml\\\")\" #>  #> $yaml$FM$include$files[[3]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.yaml\\\")\" #>  #>  #>  #>  #>  #> $yaml$FM$deployed #> [1] FALSE #>  #> $yaml$FM$code #> $yaml$FM$code$theme #> [1] \"vibrant_ink\" #>  #> $yaml$FM$code$showLineNumbers #> [1] TRUE #>  #> $yaml$FM$code$gen_file #> [1] \"run_analysis.R\" #>  #> $yaml$FM$code$gen_preamble #> [1] \"# formods automated output ------------------------------------------------\\n# https://formods.ubiquity.tools/\\nrm(list=ls())\" #>  #> $yaml$FM$code$packages #> [1] \"onbrand\" \"writexl\" #>  #>  #> $yaml$FM$notifications #> $yaml$FM$notifications$config #> $yaml$FM$notifications$config$success #> $yaml$FM$notifications$config$success$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$background #> [1] \"#5bb85b\" #>  #>  #> $yaml$FM$notifications$config$failure #> $yaml$FM$notifications$config$failure$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$background #> [1] \"#d9534f\" #>  #>  #> $yaml$FM$notifications$config$info #> $yaml$FM$notifications$config$info$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$background #> [1] \"#5bc0de\" #>  #>  #> $yaml$FM$notifications$config$warning #> $yaml$FM$notifications$config$warning$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$background #> [1] \"#f0ac4d\" #>  #>  #>  #>  #> $yaml$FM$reporting #> $yaml$FM$reporting$enabled #> [1] TRUE #>  #> $yaml$FM$reporting$content_init #> $yaml$FM$reporting$content_init$xlsx #> [1] \"rpt = list(summary = NULL,\\n           sheets  = list())\" #>  #> $yaml$FM$reporting$content_init$docx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.docx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #> $yaml$FM$reporting$content_init$pptx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.pptx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #>  #>  #> $yaml$FM$ui #> $yaml$FM$ui$button_style #> [1] \"fill\" #>  #> $yaml$FM$ui$select_size #> [1] 10 #>  #> $yaml$FM$ui$color_green #> [1] \"#00BB8A\" #>  #> $yaml$FM$ui$color_red #> [1] \"#FF475E\" #>  #> $yaml$FM$ui$color_blue #> [1] \"#0088FF\" #>  #> $yaml$FM$ui$color_purple #> [1] \"#bd2cf4\" #>  #>  #> $yaml$FM$data_meta #> $yaml$FM$data_meta$data_header #> [1] \"<span style='color:===COLOR==='><b>===NAME===<\/b><br/><font size='-3'>===LABEL===<\/font><\/span>\" #>  #> $yaml$FM$data_meta$subtext #> [1] \"===LABEL===: ===RANGE===\" #>  #> $yaml$FM$data_meta$many_sep #> [1] \",⋅⋅⋅,\" #>  #> $yaml$FM$data_meta$data_types #> $yaml$FM$data_meta$data_types$character #> $yaml$FM$data_meta$data_types$character$color #> [1] \"#DD4B39\" #>  #> $yaml$FM$data_meta$data_types$character$label #> [1] \"text\" #>  #>  #> $yaml$FM$data_meta$data_types$double #> $yaml$FM$data_meta$data_types$double$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$double$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$integer #> $yaml$FM$data_meta$data_types$integer$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$integer$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$other #> $yaml$FM$data_meta$data_types$other$color #> [1] \"black\" #>  #> $yaml$FM$data_meta$data_types$other$label #> [1] \"other\" #>  #>  #>  #>  #> $yaml$FM$labels #> $yaml$FM$labels$default_ds #> [1] \"Original data set\" #>  #>  #> $yaml$FM$user_files #> $yaml$FM$user_files$use_tmpdir #> [1] TRUE #>  #>  #> $yaml$FM$logging #> $yaml$FM$logging$enabled #> [1] TRUE #>  #> $yaml$FM$logging$timestamp #> [1] TRUE #>  #> $yaml$FM$logging$timestamp_fmt #> [1] \"%Y-%m-%d %H:%M:%S\" #>  #> $yaml$FM$logging$log_file #> [1] \"formods_log.txt\" #>  #> $yaml$FM$logging$console #> [1] TRUE #>  #>  #>  #>  #> $MC #> $MC$element_object_name #> [1] \"MB_obj\" #>  #> $MC$sources #> $MC$sources[[1]] #> $MC$sources[[1]]$source #> $MC$sources[[1]]$source$group #> [1] \"User-defined Models\" #>  #> $MC$sources[[1]]$source$name #> [1] \"rxode2 User model\" #>  #> $MC$sources[[1]]$source$description #> [1] \"User-defined rxode2 model\" #>  #> $MC$sources[[1]]$source$type #> [1] \"rxode2\" #>  #> $MC$sources[[1]]$source$obj #> [1] \"my_fcn\" #>  #> $MC$sources[[1]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.R\\\")\" #>  #>  #>  #> $MC$sources[[2]] #> $MC$sources[[2]]$source #> $MC$sources[[2]]$source$group #> [1] \"User-defined Models\" #>  #> $MC$sources[[2]]$source$name #> [1] \"NONMEM User model\" #>  #> $MC$sources[[2]]$source$description #> [1] \"User-defined NONMEM model\" #>  #> $MC$sources[[2]]$source$type #> [1] \"NONMEM\" #>  #> $MC$sources[[2]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.ctl\\\")\" #>  #>  #>  #> $MC$sources[[3]] #> $MC$sources[[3]]$source #> $MC$sources[[3]]$source$group #> [1] \"Model Library\" #>  #> $MC$sources[[3]]$source$type #> [1] \"nlmixr2lib\" #>  #> $MC$sources[[3]]$source$name #> [1] \"nlmixr2 Model Library\" #>  #>  #>  #>  #> $MC$code #> $MC$code$packages #> [1] \"rxode2\"     \"nonmem2rx\"  \"nlmixr2lib\" #>  #> $MC$code$readOnly #> [1] TRUE #>  #> $MC$code$mode #> [1] \"r\" #>  #>  #> $MC$compact #> $MC$compact$code #> [1] TRUE #>  #> $MC$compact$clip #> [1] TRUE #>  #>  #> $MC$reporting #> $MC$reporting$enabled #> [1] FALSE #>  #> $MC$reporting$priority #> [1] 1 #>  #>  #> $MC$formatting #> $MC$formatting$code #> $MC$formatting$code$width #> [1] 800 #>  #> $MC$formatting$code$height #> [1] 300 #>  #>  #> $MC$formatting$preview #> $MC$formatting$preview$width #> [1] \"800px\" #>  #> $MC$formatting$preview$height #> [1] \"500px\" #>  #>  #> $MC$formatting$input #> NULL #>  #> $MC$formatting$current_element #> $MC$formatting$current_element$width #> [1] \"200px\" #>  #> $MC$formatting$current_element$tooltip #> [1] \"Change the current model.\" #>  #> $MC$formatting$current_element$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$element_name #> $MC$formatting$element_name$width #> [1] \"200px\" #>  #>  #> $MC$formatting$button_clk_run #> $MC$formatting$button_clk_run$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_run$block #> [1] TRUE #>  #>  #> $MC$formatting$button_clk_del #> $MC$formatting$button_clk_del$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_del$block #> [1] TRUE #>  #> $MC$formatting$button_clk_del$tooltip #> [1] \"Delete the current model.\" #>  #> $MC$formatting$button_clk_del$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_save #> $MC$formatting$button_clk_save$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_save$block #> [1] TRUE #>  #> $MC$formatting$button_clk_save$tooltip #> [1] \"Save the name for the current model.\" #>  #> $MC$formatting$button_clk_save$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_clip #> $MC$formatting$button_clk_clip$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_clip$block #> [1] TRUE #>  #> $MC$formatting$button_clk_clip$tooltip #> [1] \"Copy the code to generate the current model to the clipboard.\" #>  #> $MC$formatting$button_clk_clip$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_copy #> $MC$formatting$button_clk_copy$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_copy$block #> [1] TRUE #>  #> $MC$formatting$button_clk_copy$tooltip #> [1] \"Make a copy of the current model.\" #>  #> $MC$formatting$button_clk_copy$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_new #> $MC$formatting$button_clk_new$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_new$block #> [1] TRUE #>  #> $MC$formatting$button_clk_new$tooltip #> [1] \"Create a new model.\" #>  #> $MC$formatting$button_clk_new$tooltip_position #> [1] \"right\" #>  #> $MC$formatting$button_clk_new$width #> [1] 100 #>  #>  #> $MC$formatting$catalog_selection #> $MC$formatting$catalog_selection$width #> [1] \"200px\" #>  #> $MC$formatting$catalog_selection$truncate #> [1] 65 #>  #> $MC$formatting$catalog_selection$tooltip #> [1] \"Select base model from the catalog.\" #>  #> $MC$formatting$catalog_selection$tooltip_position #> [1] \"top\" #>  #>  #> $MC$formatting$model_type_selection #> $MC$formatting$model_type_selection$width #> [1] \"200px\" #>  #> $MC$formatting$model_type_selection$tooltip #> [1] \"Select the type of the model being uploaded.\" #>  #> $MC$formatting$model_type_selection$tooltip_position #> [1] \"right\" #>  #> $MC$formatting$model_type_selection$choices #> $MC$formatting$model_type_selection$choices$rxode2 #> [1] \"rxode2 function\" #>  #> $MC$formatting$model_type_selection$choices$NONMEM #> [1] \"NONMEM (XML, lst, or ctl)\" #>  #>  #> $MC$formatting$model_type_selection$default #> [1] \"rxode2\" #>  #>  #> $MC$formatting$upload_model_file #> $MC$formatting$upload_model_file$width #> [1] \"400px\" #>  #>  #> $MC$formatting$base_from #> $MC$formatting$base_from$size #> [1] \"normal\" #>  #> $MC$formatting$base_from$status #> [1] \"primary\" #>  #> $MC$formatting$base_from$tooltip #> [1] \"You can select the base model from a catalog or upload your own\" #>  #> $MC$formatting$base_from$tooltip_position #> [1] \"bottom\" #>  #> $MC$formatting$base_from$choices #> $MC$formatting$base_from$choices$catalog #> [1] \"Model Catalog\" #>  #> $MC$formatting$base_from$choices$user #> [1] \"User-defined model\" #>  #>  #> $MC$formatting$base_from$default #> [1] \"catalog\" #>  #>  #> $MC$formatting$time_scales #> $MC$formatting$time_scales$width #> [1] \"200px\" #>  #> $MC$formatting$time_scales$default #> [1] \"weeks\" #>  #> $MC$formatting$time_scales$tooltip #> [1] \"Choose the timescale of the model.\" #>  #> $MC$formatting$time_scales$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$time_scales$choices #> $MC$formatting$time_scales$choices$months #> $MC$formatting$time_scales$choices$months$conv #> [1] \"60*60*24*4\" #>  #> $MC$formatting$time_scales$choices$months$verb #> [1] \"Months\" #>  #>  #> $MC$formatting$time_scales$choices$weeks #> $MC$formatting$time_scales$choices$weeks$conv #> [1] \"60*60*24*7\" #>  #> $MC$formatting$time_scales$choices$weeks$verb #> [1] \"Weeks\" #>  #>  #> $MC$formatting$time_scales$choices$days #> $MC$formatting$time_scales$choices$days$conv #> [1] \"60*60*24\" #>  #> $MC$formatting$time_scales$choices$days$verb #> [1] \"Days\" #>  #>  #> $MC$formatting$time_scales$choices$hours #> $MC$formatting$time_scales$choices$hours$conv #> [1] \"60*60\" #>  #> $MC$formatting$time_scales$choices$hours$verb #> [1] \"Hours\" #>  #>  #> $MC$formatting$time_scales$choices$min #> $MC$formatting$time_scales$choices$min$conv #> [1] \"60\" #>  #> $MC$formatting$time_scales$choices$min$verb #> [1] \"Minutes\" #>  #>  #> $MC$formatting$time_scales$choices$sec #> $MC$formatting$time_scales$choices$sec$conv #> [1] \"1\" #>  #> $MC$formatting$time_scales$choices$sec$verb #> [1] \"Seconds\" #>  #>  #>  #>  #>  #> $MC$labels #> $MC$labels$current_element #> NULL #>  #> $MC$labels$element_name #> NULL #>  #> $MC$labels$upload_model_file #> NULL #>  #> $MC$labels$base_from #> NULL #>  #> $MC$labels$catalog_selection #> NULL #>  #> $MC$labels$model_type_selection #> NULL #>  #> $MC$labels$time_scale #> NULL #>  #> $MC$labels$catalog_empty #> [1] \"No models were found, the catalog is empty\" #>  #> $MC$labels$save_btn #> [1] \"Save\" #>  #> $MC$labels$clip_btn #> [1] \"Code\" #>  #> $MC$labels$copy_btn #> [1] \"Copy\" #>  #> $MC$labels$del_btn #> [1] \"Delete\" #>  #> $MC$labels$new_btn #> [1] \"New\" #>  #> $MC$labels$building_model #> [1] \"Building model, be patient.\" #>  #> $MC$labels$element_name_diff #> [1] \"The model name has changed.\" #>  #> $MC$labels$model_code_diff #> [1] \"Manual changes have been made to the model.\" #>  #> $MC$labels$save_change_detected #> [1] \"You need to click on the save button for these changes to take effect.\" #>  #> $MC$labels$head_base_model #> [1] \"Starting model\" #>  #> $MC$labels$head_model_code #> [1] \"Model code\" #>  #> $MC$labels$head_time_scale #> [1] \"Model time-scale\" #>  #>  #> $MC$errors #> $MC$errors$no_model_found #> [1] \"No model was found.\" #>  #> $MC$errors$base_model_build_failed #> [1] \"The base model build failed.\" #>  #> $MC$errors$manual_model_update_failed #> [1] \"Manual model update failed.\" #>  #> $MC$errors$user_file_upload_failed #> [1] \"User-defined model failed.\" #>  #> $MC$errors$nlmixr2lib_not_found #> [1] \"The nlmixr2lib package was not found. This library will not be available.\" #>  #> $MC$errors$selected_id_bad_list #> [1] \"Unable to find a list for the selected component.\" #>  #> $MC$errors$selected_id_bad_row #> [1] \"Selected component should have 1 row.\" #>  #>  #> $MC$tooltips #> $MC$tooltips$include #> [1] TRUE #>  #> $MC$tooltips$elements #> [1] \"Show model components\" #>  #> $MC$tooltips$show_code #> [1] \"Show model code\" #>  #> $MC$tooltips$url_rxode #> [1] \"https://nlmixr2.github.io/rxode2/articles/rxode2-syntax.html\" #>  #> $MC$tooltips$url_model_types #> [1] \"https://ruminate.ubiquity.tools/articles/model_builder.html#model-catalog\" #>  #> $MC$tooltips$components #> $MC$tooltips$components$input_tip #> [1] \"My tool tip\" #>  #>  #>  #>  #> $MB #> $MB$button_counters #> $MB$button_counters$button_clk_save #> [1] 0 #>  #> $MB$button_counters$button_clk_clip #> [1] 0 #>  #> $MB$button_counters$button_clk_del #> [1] 0 #>  #> $MB$button_counters$button_clk_copy #> [1] 0 #>  #> $MB$button_counters$button_clk_new #> [1] 0 #>  #>  #> $MB$ui_hold #> $MB$ui_hold$button_clk_save #> [1] FALSE #>  #> $MB$ui_hold$button_clk_clip #> [1] FALSE #>  #> $MB$ui_hold$button_clk_del #> [1] FALSE #>  #> $MB$ui_hold$button_clk_copy #> [1] FALSE #>  #> $MB$ui_hold$button_clk_new #> [1] FALSE #>  #> $MB$ui_hold$catalog_selection #> [1] FALSE #>  #> $MB$ui_hold$base_from #> [1] FALSE #>  #> $MB$ui_hold$element_name #> [1] FALSE #>  #> $MB$ui_hold$time_scale #> [1] FALSE #>  #> $MB$ui_hold$model_type_selection #> [1] FALSE #>  #> $MB$ui_hold$ui_select_element #> [1] FALSE #>  #> $MB$ui_hold$ui_mb_model #> [1] FALSE #>  #> $MB$ui_hold$uploaded_model #> [1] FALSE #>  #> $MB$ui_hold$element_selection #> [1] FALSE #>  #>  #> $MB$ui_ids #>  [1] \"button_clk_save\"      \"button_clk_clip\"      \"button_clk_del\"       #>  [4] \"button_clk_copy\"      \"button_clk_new\"       \"catalog_selection\"    #>  [7] \"base_from\"            \"element_name\"         \"time_scale\"           #> [10] \"model_type_selection\" \"ui_select_element\"    \"ui_mb_model\"          #> [13] \"model_type_selection\" \"uploaded_model\"       \"element_selection\"    #>  #> $MB$isgood #> [1] TRUE #>  #> $MB$ui_ele #> [1] \"catalog_selection\" \"base_from\"         \"element_name\"      #> [4] \"time_scale\"        #>  #> $MB$element_cntr #> [1] 1 #>  #> $MB$model_type_selection #> [1] \"rxode2\" #>  #> $MB$suggested #> $MB$suggested$found #> [1] TRUE #>  #> $MB$suggested$pkgs #> $MB$suggested$pkgs$rxode2 #> $MB$suggested$pkgs$rxode2$found #> [1] TRUE #>  #> $MB$suggested$pkgs$rxode2$msg #> [1] \"\" #>  #>  #> $MB$suggested$pkgs$nonmem2rx #> $MB$suggested$pkgs$nonmem2rx$found #> [1] TRUE #>  #> $MB$suggested$pkgs$nonmem2rx$msg #> [1] \"\" #>  #>  #> $MB$suggested$pkgs$nlmixr2lib #> $MB$suggested$pkgs$nlmixr2lib$found #> [1] TRUE #>  #> $MB$suggested$pkgs$nlmixr2lib$msg #> [1] \"\" #>  #>  #>  #>  #> $MB$model_catalog #> $MB$model_catalog$sources #> $MB$model_catalog$sources[[1]] #> $MB$model_catalog$sources[[1]]$source #> $MB$model_catalog$sources[[1]]$source$group #> [1] \"User-defined Models\" #>  #> $MB$model_catalog$sources[[1]]$source$name #> [1] \"rxode2 User model\" #>  #> $MB$model_catalog$sources[[1]]$source$description #> [1] \"User-defined rxode2 model\" #>  #> $MB$model_catalog$sources[[1]]$source$type #> [1] \"rxode2\" #>  #> $MB$model_catalog$sources[[1]]$source$obj #> [1] \"my_fcn\" #>  #> $MB$model_catalog$sources[[1]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.R\\\")\" #>  #>  #>  #> $MB$model_catalog$sources[[2]] #> $MB$model_catalog$sources[[2]]$source #> $MB$model_catalog$sources[[2]]$source$group #> [1] \"User-defined Models\" #>  #> $MB$model_catalog$sources[[2]]$source$name #> [1] \"NONMEM User model\" #>  #> $MB$model_catalog$sources[[2]]$source$description #> [1] \"User-defined NONMEM model\" #>  #> $MB$model_catalog$sources[[2]]$source$type #> [1] \"NONMEM\" #>  #> $MB$model_catalog$sources[[2]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.ctl\\\")\" #>  #>  #>  #> $MB$model_catalog$sources[[3]] #> $MB$model_catalog$sources[[3]]$source #> $MB$model_catalog$sources[[3]]$source$group #> [1] \"Model Library\" #>  #> $MB$model_catalog$sources[[3]]$source$type #> [1] \"nlmixr2lib\" #>  #> $MB$model_catalog$sources[[3]]$source$name #> [1] \"nlmixr2 Model Library\" #>  #>  #>  #>  #> $MB$model_catalog$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models #>  #> $MB$model_catalog$select_group #> $MB$model_catalog$select_group$`Model Library` #> $MB$model_catalog$select_group$`Model Library`$PK_1cmt #> [1] \"mod_1\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_1cmt_des #> [1] \"mod_2\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt #> [1] \"mod_3\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_des #> [1] \"mod_4\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_no_depot #> [1] \"mod_5\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_tdcl_des #> [1] \"mod_6\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_3cmt #> [1] \"mod_7\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_3cmt_des #> [1] \"mod_8\" #>  #> $MB$model_catalog$select_group$`Model Library`$indirect_0cpt_transitEx #> [1] \"mod_9\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_mAb_Davda_2014 #> [1] \"mod_10\" #>  #> $MB$model_catalog$select_group$`Model Library`$CarlssonPetri_2021_liraglutide #> [1] \"mod_11\" #>  #> $MB$model_catalog$select_group$`Model Library`$Kovalenko_2020_dupilumab #> [1] \"mod_12\" #>  #> $MB$model_catalog$select_group$`Model Library`$Soehoel_2022_tralokinumab #> [1] \"mod_13\" #>  #> $MB$model_catalog$select_group$`Model Library`$Zhu_2017_lebrikizumab #> [1] \"mod_14\" #>  #> $MB$model_catalog$select_group$`Model Library`$oncology_sdm_lobo_2002 #> [1] \"mod_15\" #>  #> $MB$model_catalog$select_group$`Model Library`$oncology_xenograft_simeoni_2004 #> [1] \"mod_16\" #>  #>  #>  #> $MB$model_catalog$select_plain #> $MB$model_catalog$select_plain$PK_1cmt #> [1] \"mod_1\" #>  #> $MB$model_catalog$select_plain$PK_1cmt_des #> [1] \"mod_2\" #>  #> $MB$model_catalog$select_plain$PK_2cmt #> [1] \"mod_3\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_des #> [1] \"mod_4\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_no_depot #> [1] \"mod_5\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_tdcl_des #> [1] \"mod_6\" #>  #> $MB$model_catalog$select_plain$PK_3cmt #> [1] \"mod_7\" #>  #> $MB$model_catalog$select_plain$PK_3cmt_des #> [1] \"mod_8\" #>  #> $MB$model_catalog$select_plain$indirect_0cpt_transitEx #> [1] \"mod_9\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_mAb_Davda_2014 #> [1] \"mod_10\" #>  #> $MB$model_catalog$select_plain$CarlssonPetri_2021_liraglutide #> [1] \"mod_11\" #>  #> $MB$model_catalog$select_plain$Kovalenko_2020_dupilumab #> [1] \"mod_12\" #>  #> $MB$model_catalog$select_plain$Soehoel_2022_tralokinumab #> [1] \"mod_13\" #>  #> $MB$model_catalog$select_plain$Zhu_2017_lebrikizumab #> [1] \"mod_14\" #>  #> $MB$model_catalog$select_plain$oncology_sdm_lobo_2002 #> [1] \"mod_15\" #>  #> $MB$model_catalog$select_plain$oncology_xenograft_simeoni_2004 #> [1] \"mod_16\" #>  #>  #> $MB$model_catalog$select_subtext #>  [1] \"One compartment PK model with linear clearance\"                                                                           #>  [2] \"One compartment PK model with linear clearance using differential equations\"                                              #>  [3] \"Two compartment PK model with linear clearance\"                                                                           #>  [4] \"Two compartment PK model with linear clearance using differential equations\"                                              #>  [5] \"Two compartment PK model with linear clearance using differential equations\"                                              #>  [6] \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\" #>  [7] \"Three compartment PK model with linear clearance\"                                                                         #>  [8] \"Three compartment PK model with linear clearance using differential equations\"                                            #>  [9] \"Two compartment PK model with Michealis-Menten clearance using differential equations\"                                    #> [10] \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"                            #> [11] \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"                                                                #> [12] \"Dupilumab PK model (Kovalenko 2020)\"                                                                                      #> [13] \"Tralokinumab PK model (Soehoel 2022)\"                                                                                     #> [14] \"Lebrikizumab PK model (Zhu 2017)\"                                                                                         #> [15] \"Signal transduction model for delayed concentration effects on cancer cell growth\"                                        #> [16] \"Oncology tumor growth model in xenograft models\"                                                                          #>  #> $MB$model_catalog$msgs #> NULL #>  #> $MB$model_catalog$hasmdl #> [1] TRUE #>  #> $MB$model_catalog$isgood #> [1] TRUE #>  #>  #> $MB$elements #> $MB$elements$element_1 #> $MB$elements$element_1$isgood #> [1] TRUE #>  #> $MB$elements$element_1$ui #> $MB$elements$element_1$ui$ui_mb_model #> [1] \"\" #>  #> $MB$elements$element_1$ui$time_scale #> [1] \"weeks\" #>  #> $MB$elements$element_1$ui$element_name #> [1] \"Model 1\" #>  #> $MB$elements$element_1$ui$catalog_selection #> [1] \"mod_1\" #>  #> $MB$elements$element_1$ui$base_from #> [1] \"catalog\" #>  #>  #> $MB$elements$element_1$id #> [1] \"element_1\" #>  #> $MB$elements$element_1$idx #> [1] 1 #>  #> $MB$elements$element_1$fcn_obj_name #> [1] \"MB_obj_1_fcn\" #>  #> $MB$elements$element_1$rx_obj_name #> [1] \"MB_obj_1_rx\" #>  #> $MB$elements$element_1$msgs #> NULL #>  #> $MB$elements$element_1$code_previous #> [1] \"\" #>  #> $MB$elements$element_1$update_model_code #> [1] FALSE #>  #> $MB$elements$element_1$components_table #> data frame with 0 columns and 0 rows #>  #> $MB$elements$element_1$selected_component_id #> NULL #>  #> $MB$elements$element_1$components_list #> list() #>  #> $MB$elements$element_1$checksum #> [1] \"4d23d48e7503c4f405af82cd71527ff3\" #>  #> $MB$elements$element_1$base_model_name #> [1] \"\" #>  #> $MB$elements$element_1$base_model #> [1] \"\" #>  #>  #>  #> $MB$current_element #> [1] \"element_1\" #>  #> $MB$checksum #> [1] \"a8d5d330f91feb7be769e08518458dd8\" #>  #>  #> $MOD_TYPE #> [1] \"MB\" #>  #> $id #> [1] \"MB\" #>  #> $FM_yaml_file #> [1] \"/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/formods/templates/formods.yaml\" #>  #> $MOD_yaml_file #> [1] \"/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/RtmpYagqQE/temp_libpathc4691c5677bf/ruminate/templates/MB.yaml\" #>  #> $shiny_token #> [1] \"non_shiny\" #>"},{"path":"/reference/MB_mod_ok.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes Sure Suggests are Installed — MB_mod_ok","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"Simple check make sure suggested packages installed.","code":""},{"path":"/reference/MB_mod_ok.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"","code":"MB_mod_ok(pkgs = c(\"rxode2\", \"nonmem2rx\", \"nlmixr2lib\"))"},{"path":"/reference/MB_mod_ok.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"pkgs List suggested packages (default \"rxode2\", \"nonmem2rx\" \"nlmixr2lib\")","code":""},{"path":"/reference/MB_mod_ok.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"Boolean value indicating module OK","code":""},{"path":"/reference/MB_mod_ok.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"","code":"MB_mod_ok() #> [1] FALSE"},{"path":"/reference/MB_new_element.html","id":null,"dir":"Reference","previous_headings":"","what":"New Model Building Model — MB_new_element","title":"New Model Building Model — MB_new_element","text":"Appends new empty model MB state object makes new model active model.","code":""},{"path":"/reference/MB_new_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"New Model Building Model — MB_new_element","text":"","code":"MB_new_element(state)"},{"path":"/reference/MB_new_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"New Model Building Model — MB_new_element","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_new_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"New Model Building Model — MB_new_element","text":"MB state object containing new model model set current active model. See help MB_fetch_state() model format.","code":""},{"path":"/reference/MB_new_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"New Model Building Model — MB_new_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_set_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the Value for the  Current model — MB_set_current_element","title":"Sets the Value for the  Current model — MB_set_current_element","text":"Takes MB state returns current active model","code":""},{"path":"/reference/MB_set_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the Value for the  Current model — MB_set_current_element","text":"","code":"MB_set_current_element(state, element)"},{"path":"/reference/MB_set_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the Value for the  Current model — MB_set_current_element","text":"state MB state MB_fetch_state() element Element list MB_fetch_current_element()","code":""},{"path":"/reference/MB_set_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the Value for the  Current model — MB_set_current_element","text":"MB state object current model set using supplied value.","code":""},{"path":"/reference/MB_set_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the Value for the  Current model — MB_set_current_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_test_catalog.html","id":null,"dir":"Reference","previous_headings":"","what":"Tests the Model Catalog — MB_test_catalog","title":"Tests the Model Catalog — MB_test_catalog","text":"Reads models catalog attempts build .","code":""},{"path":"/reference/MB_test_catalog.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tests the Model Catalog — MB_test_catalog","text":"","code":"MB_test_catalog(state, as_cran = FALSE, verbose = TRUE)"},{"path":"/reference/MB_test_catalog.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tests the Model Catalog — MB_test_catalog","text":"state MB state MB_fetch_state() as_cran Boolean indicate running CRAN verbose Boolean indicate messages displayed.","code":""},{"path":"/reference/MB_test_catalog.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tests the Model Catalog — MB_test_catalog","text":"List following attributes: isgood: Boolean varaible indicating models catalog passed test. msgs: Messages indicating test successful .","code":""},{"path":"/reference/MB_test_catalog.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tests the Model Catalog — MB_test_catalog","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_test_mksession.html","id":null,"dir":"Reference","previous_headings":"","what":"Populate Session Data for Module Testing — MB_test_mksession","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"Populates supplied session variable testing.","code":""},{"path":"/reference/MB_test_mksession.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"","code":"MB_test_mksession(session, id = \"MB\", full_session = TRUE)"},{"path":"/reference/MB_test_mksession.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"session Shiny session variable (app) list (outside app) id ID string corresponds ID used call modules UI elements full_session Boolean indicate full test session created (default TRUE).","code":""},{"path":"/reference/MB_test_mksession.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"list following elements isgood: Boolean indicating exit status function. session: value Shiny session variable (app) list (outside app) initialization. input: value shiny input end session initialization. state: App state. rsc: react_state components.","code":""},{"path":"/reference/MB_test_mksession.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"","code":"sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a"},{"path":"/reference/MB_update_checksum.html","id":null,"dir":"Reference","previous_headings":"","what":"Update MB Module Checksum — MB_update_checksum","title":"Update MB Module Checksum — MB_update_checksum","text":"Takes MB state updates checksum used trigger downstream updates","code":""},{"path":"/reference/MB_update_checksum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update MB Module Checksum — MB_update_checksum","text":"","code":"MB_update_checksum(state)"},{"path":"/reference/MB_update_checksum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update MB Module Checksum — MB_update_checksum","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_update_checksum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update MB Module Checksum — MB_update_checksum","text":"MB state object checksum updated","code":""},{"path":"/reference/MB_update_checksum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update MB Module Checksum — MB_update_checksum","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = MB_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 session = sess_res$session input   = sess_res$input  # We also need a state variable state = sess_res$state  state = MB_update_checksum(state)"},{"path":"/reference/MB_update_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Updates Current Element with rxode2 Model — MB_update_model","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"Takes rxode2 object updates model components current element.","code":""},{"path":"/reference/MB_update_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"","code":"MB_update_model(state, session, current_ele, rx_obj, note, reset = FALSE)"},{"path":"/reference/MB_update_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"state MB state MB_fetch_state() session Shiny session variable current_ele MB model element MB_fetch_current_element() rx_obj rxode2 model rxode2::rxode2() note text indicating update (e.g. \"added parameter\") reset boolean indicating element needs reset (.e. change base model) default: FALSE.","code":""},{"path":"/reference/MB_update_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"current_element model attached","code":""},{"path":"/reference/MB_update_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: ef47c330d3ab2d804b6cec26d53b9683 #> → MB: module checksum updated:baf2de6eb2f5b0af33c6d3182c9ce61a #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 8fe63af71c1f0c6b582646ec3c4c31b0 #> → MB: module checksum updated:bc8090bd2fe05a30d8e0d5b5ef604e24 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 8ad73a6e1f886e917caa3f8d91325a85 #> → MB: module checksum updated:e195ea7b646ec2f655c61dc4a1cd398a  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/NCA_Server.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Non-Compartmental Analysis State — NCA_Server","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"Merges default app options changes made UI","code":""},{"path":"/reference/NCA_Server.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"","code":"NCA_Server(   id,   FM_yaml_file = system.file(package = \"formods\", \"templates\", \"formods.yaml\"),   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\"),   id_ASM = \"ASM\",   id_UD = \"UD\",   id_DW = \"DW\",   deployed = FALSE,   react_state = NULL )"},{"path":"/reference/NCA_Server.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"id ID string corresponds ID used call modules UI elements FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id_ASM ID string app state management module used save load app states id_UD ID string upload data module used save load app states id_DW ID string data wrangling module used save load app states deployed Boolean variable indicating whether app deployed . react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/NCA_Server.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. NCA: isgood: Boolean object indicating file successfully loaded. checksum: MD5 sum contents element can used detect changes state. MOD_TYPE: Character data containing type module \"NCA\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/NCA_Server.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"","code":"if(interactive()){ # original file: inst/templates/ruminate.R library(formods) library(ruminate)  # These are suggested packages library(shinydashboard) #library(ggpubr) #library(plotly) #library(shinybusy) library(prompter) #library(utils)  tags$style(\"@import url(https://use.fontawesome.com/releases/v6.4.0/css/all.css);\")  # You can copy these locally and customize them for your own needs. Simply # change the assignment to the local copy you've modified. formods.yaml  = system.file(package=\"formods\",  \"templates\",  \"formods.yaml\") ASM.yaml      = system.file(package=\"formods\",  \"templates\",  \"ASM.yaml\") UD.yaml       = system.file(package=\"formods\",  \"templates\",  \"UD.yaml\") DW.yaml       = system.file(package=\"formods\",  \"templates\",  \"DW.yaml\") FG.yaml       = system.file(package=\"formods\",  \"templates\",  \"FG.yaml\") NCA.yaml      = system.file(package=\"ruminate\", \"templates\",  \"NCA.yaml\")   # Name of  file to indicate we need to load testing data ftmptest = file.path(tempdir(), \"ruminate.test\")   # Making sure that the deployed object is created if(!exists(\"deployed\")){   deployed = FALSE }  # Making sure that the run_dev object is created if(file.exists(file.path(tempdir(), \"RUMINTE_DEVELOPMENT\"))){   run_dev  = TRUE }else{   run_dev  = FALSE }  # If the SETUP.R file exists we source it  if(file.exists(\"SETUP.R\")){   source(\"SETUP.R\") }   # If the DEPLOYED file marker existrs we set deployed to TRUE if(file.exists(\"DEPLOYED\")){   deployed = TRUE }  CSS <- \" .wrapfig {   float: right;   shape-margin: 20px;   margin-right: 20px;   margin-bottom: 20px; } \"  #https://fontawesome.com/icons?from=io logo_url =   \"https://raw.githubusercontent.com/john-harrold/ruminate/main/man/figures/logo.png\" data_url =   \"https://github.com/john-harrold/formods/raw/master/inst/test_data/TEST_DATA.xlsx\" run_url  =   \"https://runruminate.ubiquity.tools/\" use_url  =   \"https://useruminate.ubiquity.tools/\" main_url =   \"https://ruminate.ubiquity.tools/\" issue_url =   \"https://github.com/john-harrold/ruminate/issues\"  intro_text = tags$p( \"Ruminate is a shiny module for pharmacometric data processing, visualization, and analysis. It consists of separate shiny modules that provide interfaces into common R packages and provides the underlying code. This is done to facilitate usage of those packages and to provide reproducible analyses.\" , tags$li( \"To find out more visit  \",         tags$a(\"ruminate.ubiquity.tools\", href=main_url),\"\"), tags$li( \"To give it a try you can download a test dataset \",         tags$a(\"here\", href=data_url),\"\"), tags$li( \"Go to  \",         tags$a(\"useruminate.ubiquity.tools\", href=use_url),\" for a video         demonstrating how to use ruminate\"), tags$li( \"If you run into any problems, have questions, or want a feature please         visit the \",         tags$a(\"issues\", href=issue_url),\" page\") )   ui <- shinydashboard::dashboardPage(   skin=\"black\",   shinydashboard::dashboardHeader(title=\"ruminate\"),   shinydashboard::dashboardSidebar(      shinydashboard::sidebarMenu(        shinydashboard::menuItem(\"Load/Save\",                                 tabName=\"loadsave\",                                 icon=icon(\"arrow-down-up-across-line\")) ,        shinydashboard::menuItem(\"Transform Data\", tabName=\"wrangle\", icon=icon(\"shuffle\")),        shinydashboard::menuItem(\"Visualize\",      tabName=\"plot\",    icon=icon(\"chart-line\")),        shinydashboard::menuItem(\"NCA\",            tabName=\"nca\",     icon=icon(\"chart-area\")),        shinydashboard::menuItem(\"App Info\",       tabName=\"sysinfo\", icon=icon(\"book-medical\"))      )   ),   shinydashboard::dashboardBody(   tags$head(     tags$style(HTML(CSS))   ),     shinydashboard::tabItems(        shinydashboard::tabItem(tabName=\"nca\",                shinydashboard::box(title=\"Run Non-Compartmental Analysis\", width=12,                fluidRow( prompter::use_prompt(),                column(width=12,                htmlOutput(NS(\"NCA\",  \"NCA_ui_compact\")))))                ),        shinydashboard::tabItem(tabName=\"loadsave\",          #     shinydashboard::box(title=NULL, width=12,                shinydashboard::tabBox(                  width = 12,                  title = NULL,                  shiny::tabPanel(id=\"load_data\",                           title=tagList(shiny::icon(\"file-arrow-up\"),                                         \"Load Data\"),                    fluidRow(                      column(width=6,                        div(style=\"display:inline-block;width:100%\",                        htmlOutput(NS(\"UD\", \"ui_ud_load_data\"))),                        htmlOutput(NS(\"UD\", \"ui_ud_clean\")),                        htmlOutput(NS(\"UD\", \"ui_ud_select_sheets\")),                        htmlOutput(NS(\"UD\", \"ui_ud_text_load_result\"))),                      column(width=6,                          tags$p(                              tags$img(                              class = \"wrapfig\",                              src   = logo_url,                              width = 150,                              alt = \"formods logo\" ),                          intro_text                          ))                    ),                  fluidRow(                    column(width=12,                           div(style=\"display:inline-block;vertical-align:top\",                                     htmlOutput(NS(\"UD\", \"ui_ud_data_preview\")))                           ))                  ),                  shiny::tabPanel(id=\"save_state\",                           title=tagList(shiny::icon(\"arrow-down-up-across-line\"),                                         \"Save or Load Analysis\"),                  fluidRow(                    column(width=5,                           div(style=\"display:inline-block;vertical-align:top\",                    htmlOutput(NS(\"ASM\", \"ui_asm_compact\"))                    ))                    )                  )                )          #     ),                ),        shinydashboard::tabItem(tabName=\"wrangle\",                shinydashboard::box(title=\"Transform and Create Views of Your Data\", width=12,                fluidRow(                column(width=12,                htmlOutput(NS(\"DW\",  \"DW_ui_compact\")))))                ),        shinydashboard::tabItem(tabName=\"plot\",                shinydashboard::box(title=\"Visualize Data\", width=12,                htmlOutput(NS(\"FG\",  \"FG_ui_compact\")))),        shinydashboard::tabItem(tabName=\"sysinfo\",          #     box(title=\"System Details\", width=12,                shinydashboard::tabBox(                  width = 12,                  title = NULL,                  shiny::tabPanel(id=\"sys_packages\",                           title=tagList(shiny::icon(\"box-open\"),                                         \"Installed Packages\"),                  htmlOutput(NS(\"ASM\", \"ui_asm_sys_packages\"))                  ),                  shiny::tabPanel(id=\"sys_modules\",                           title=tagList(shiny::icon(\"cubes\"),                                         \"Loaded Modules\"),                  htmlOutput(NS(\"ASM\", \"ui_asm_sys_modules\"))                  ),                  shiny::tabPanel(id=\"sys_log\",                           title=tagList(shiny::icon(\"clipboard-list\"),                                         \"Log\"),                  verbatimTextOutput(NS(\"ASM\", \"ui_asm_sys_log\"))                  ),                  shiny::tabPanel(id=\"sys_options\",                           title=tagList(shiny::icon(\"sliders\"),                                         \"R Options\"),                  htmlOutput(NS(\"ASM\", \"ui_asm_sys_options\"))                  )          #       )                ))       )     )   )  # Main app server server <- function(input, output, session) {    # Empty reactive object to track and react to   # changes in the module state outside of the module   react_FM = reactiveValues()    # Module IDs and the order they are needed for code generation   mod_ids = c(\"UD\", \"DW\", \"FG\", \"NCA\", \"MB\")    # If the ftmptest file is present we load test data   if(file.exists(ftmptest)){     NCA_test_mksession(       session,       id     = \"NCA\",       id_UD  = \"UD\",       id_DW  = \"DW\",       id_ASM = \"ASM\"     )   }    # Module servers   formods::ASM_Server( id=\"ASM\",                        deployed      = deployed,                        react_state   = react_FM,                        FM_yaml_file  = formods.yaml,                        MOD_yaml_file = ASM.yaml,                        mod_ids       = mod_ids)   formods::UD_Server(  id =\"UD\", id_ASM = \"ASM\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = UD.yaml,                        FM_yaml_file     = formods.yaml)   formods::DW_Server(  id=\"DW\",       id_ASM = \"ASM\",                        id_UD = \"UD\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = DW.yaml,                        FM_yaml_file     = formods.yaml)   formods::FG_Server(  id=\"FG\",     id_ASM = \"ASM\",                        id_UD = \"UD\", id_DW = \"DW\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = FG.yaml,                        FM_yaml_file     = formods.yaml)   ruminate::NCA_Server(id    =\"NCA\", id_ASM = \"ASM\",                        id_UD = \"UD\", id_DW  = \"DW\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = NCA.yaml,                        FM_yaml_file     = formods.yaml)  }  shinyApp(ui, server) }"},{"path":"/reference/NCA_add_int.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds Analysis Interval to Current Analysis — NCA_add_int","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"Takes start time, stop time, NCA parameters adds intervals table","code":""},{"path":"/reference/NCA_add_int.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"","code":"NCA_add_int(state, interval_start, interval_stop, nca_parameters)"},{"path":"/reference/NCA_add_int.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"state NCA state NCA_fetch_state() interval_start Interval start time (numeric). interval_stop Interval stop time (numeric). nca_parameters list NCA parameters interval","code":""},{"path":"/reference/NCA_add_int.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"State interval added current analysis.","code":""},{"path":"/reference/NCA_append_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Report Elements — NCA_append_report","title":"Append Report Elements — NCA_append_report","text":"Takes NCA state object appends reportable elements specified report type. NCA analyses \"good\" state reported. good state ignored.","code":""},{"path":"/reference/NCA_append_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Report Elements — NCA_append_report","text":"","code":"NCA_append_report(state, rpt, rpttype, gen_code_only = FALSE)"},{"path":"/reference/NCA_append_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Report Elements — NCA_append_report","text":"state NCA state NCA_fetch_state() rpt Report current content report appended function. details structure see documentation FM_generate_report. rpttype Type report generate (supported \"xlsx\", \"pptx\", \"docx\"). gen_code_only Boolean value indicating code generated (FALSE).","code":""},{"path":"/reference/NCA_append_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Report Elements — NCA_append_report","text":"list containing following elements isgood:    Return status function. hasrptele: Boolean indicator module reportable elements. code:      Code create report elements. msgs:      Messages passed back user. rpt:       Report additions passed back user.","code":""},{"path":[]},{"path":"/reference/NCA_append_report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append Report Elements — NCA_append_report","text":"","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:e0536abc6e0ba1b0e4f728cc154a5574 state = sess_res$state  # here we need an empty report object for tabular data rpt = list(summary = list(), sheets=list())  # Now we append the report indicating we want # Excel output: rpt_res = NCA_append_report(state,  rpt           = rpt,  rpttype       = \"xlsx\",  gen_code_only = TRUE)  # Shows if report elements are present rpt_res$hasrptele #> [1] TRUE  # Code chunk to generate report element cat(paste(rpt_res$code, collapse=\"\\n\")) #> rpt[[\"sheets\"]][[\"NCA_1_RES\"]]=NCA_1_table_ind_params[[\"one_table\"]] #> rpt[[\"summary\"]] = rbind(rpt[[\"summary\"]], #>   data.frame( #>     Sheet_Name=\"NCA_1_RES\", #>     Description=\"PK NCA Analysis Example (indiviudal results)\" #>   ) #> ) #> rpt[[\"sheets\"]][[\"NCA_1_RAW\"]]=as.data.frame(NCA_1_res) #> rpt[[\"summary\"]] = rbind(rpt[[\"summary\"]], #>   data.frame( #>     Sheet_Name=\"NCA_1_RAW\", #>     Description=\"PK NCA Analysis Example (raw NCA results)\" #>   ) #> )"},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"Compiles Metadata PKNCA","code":""},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"","code":"NCA_fetch_PKNCA_meta()"},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"Dataframe containing PKCNA metadata NCA parameters.","code":""},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"","code":"PKNCA_meta = NCA_fetch_PKNCA_meta() utils::head(PKNCA_meta) #> $parameters #>                parameter       unit_type #> 1                  start            time #> 2                    end            time #> 3                auclast             auc #> 4                 aucall             auc #> 5               aumclast            aumc #> 6                aumcall            aumc #> 7            aucint.last             auc #> 8       aucint.last.dose             auc #> 9             aucint.all             auc #> 10       aucint.all.dose             auc #> 11                    c0            conc #> 12                  cmax            conc #> 13                  cmin            conc #> 14                  tmax            time #> 15                 tlast            time #> 16                tfirst            time #> 17             clast.obs            conc #> 18               cl.last       clearance #> 19                cl.all       clearance #> 20                     f        fraction #> 21              mrt.last            time #> 22           mrt.iv.last            time #> 23              vss.last          volume #> 24           vss.iv.last          volume #> 25                   cav            conc #> 26               ctrough            conc #> 27                cstart            conc #> 28                   ptr        fraction #> 29                  tlag            time #> 30              deg.fluc               % #> 31                 swing               % #> 32                  ceoi            conc #> 33  aucabove.predose.all             auc #> 34   aucabove.trough.all             auc #> 35                    ae          amount #> 36              clr.last renal_clearance #> 37               clr.obs renal_clearance #> 38              clr.pred renal_clearance #> 39                    fe        fraction #> 40        sparse_auclast             auc #> 41            time_above            time #> 42             aucivlast             auc #> 43              aucivall             auc #> 44         aucivint.last             auc #> 45          aucivint.all             auc #> 46        aucivpbextlast               % #> 47         aucivpbextall               % #> 48    aucivpbextint.last               % #> 49     aucivpbextint.all               % #> 50             half.life            time #> 51             r.squared        unitless #> 52         adj.r.squared        unitless #> 53              lambda.z    inverse_time #> 54   lambda.z.time.first            time #> 55     lambda.z.n.points           count #> 56            clast.pred            conc #> 57            span.ratio        fraction #> 58        thalf.eff.last            time #> 59     thalf.eff.iv.last            time #> 60              kel.last    inverse_time #> 61           kel.iv.last    inverse_time #> 62            aucinf.obs             auc #> 63           aucinf.pred             auc #> 64           aumcinf.obs            aumc #> 65          aumcinf.pred            aumc #> 66        aucint.inf.obs             auc #> 67   aucint.inf.obs.dose             auc #> 68       aucint.inf.pred             auc #> 69  aucint.inf.pred.dose             auc #> 70          aucivinf.obs             auc #> 71         aucivinf.pred             auc #> 72     aucivpbextinf.obs               % #> 73    aucivpbextinf.pred               % #> 74           aucpext.obs               % #> 75          aucpext.pred               % #> 76                cl.obs       clearance #> 77               cl.pred       clearance #> 78               mrt.obs            time #> 79              mrt.pred            time #> 80            mrt.iv.obs            time #> 81           mrt.iv.pred            time #> 82            mrt.md.obs            time #> 83           mrt.md.pred            time #> 84                vz.obs          volume #> 85               vz.pred          volume #> 86               vss.obs          volume #> 87              vss.pred          volume #> 88            vss.iv.obs          volume #> 89           vss.iv.pred          volume #> 90            vss.md.obs          volume #> 91           vss.md.pred          volume #> 92                vd.obs          volume #> 93               vd.pred          volume #> 94         thalf.eff.obs            time #> 95        thalf.eff.pred            time #> 96      thalf.eff.iv.obs            time #> 97     thalf.eff.iv.pred            time #> 98               kel.obs    inverse_time #> 99              kel.pred    inverse_time #> 100           kel.iv.obs    inverse_time #> 101          kel.iv.pred    inverse_time #> 102           auclast.dn    auc_dosenorm #> 103            aucall.dn    auc_dosenorm #> 104        aucinf.obs.dn    auc_dosenorm #> 105       aucinf.pred.dn    auc_dosenorm #> 106          aumclast.dn   aumc_dosenorm #> 107           aumcall.dn   aumc_dosenorm #> 108       aumcinf.obs.dn   aumc_dosenorm #> 109      aumcinf.pred.dn   aumc_dosenorm #> 110              cmax.dn   conc_dosenorm #> 111              cmin.dn   conc_dosenorm #> 112         clast.obs.dn   conc_dosenorm #> 113        clast.pred.dn   conc_dosenorm #> 114               cav.dn   conc_dosenorm #> 115           ctrough.dn   conc_dosenorm #>                                                 pretty_name data_type #> 1                                            Interval Start  interval #> 2                                              Interval End  interval #> 3                                                   AUClast  interval #> 4                                                    AUCall  interval #> 5                                                 AUMC,last  interval #> 6                                                  AUMC,all  interval #> 7                   AUCint (based on AUClast extrapolation)  interval #> 8       AUCint (based on AUClast extrapolation, dose-aware)  interval #> 9                    AUCint (based on AUCall extrapolation)  interval #> 10       AUCint (based on AUCall extrapolation, dose-aware)  interval #> 11                                                       C0  interval #> 12                                                     Cmax  interval #> 13                                                     Cmin  interval #> 14                                                     Tmax  interval #> 15                                                    Tlast  interval #> 16                                                   Tfirst  interval #> 17                                                    Clast  interval #> 18                                    CL (based on AUClast)  interval #> 19                                     CL (based on AUCall)  interval #> 20                                          Bioavailability  interval #> 21                                   MRT (based on AUClast)  interval #> 22                    MRT (for IV dosing, based on AUClast)  interval #> 23                                   Vss (based on AUClast)  interval #> 24                    Vss (for IV dosing, based on AUClast)  interval #> 25                                                      Cav  interval #> 26                                                  Ctrough  interval #> 27                                                   Cstart  interval #> 28                                     Peak-to-trough ratio  interval #> 29                                                     Tlag  interval #> 30                                    Degree of fluctuation  interval #> 31                                                    Swing  interval #> 32                                                     Ceoi  interval #> 33                                                AUC,above  interval #> 34                                                AUC,above  interval #> 35                                          Amount excreted  interval #> 36                           Renal clearance (from AUClast)  interval #> 37                        Renal clearance (from AUCinf,obs)  interval #> 38                       Renal clearance (from AUCinf,pred)  interval #> 39                                        Fraction excreted  interval #> 40                                           Sparse AUClast  interval #> 41                                 Time above Concentration  interval #> 42                                      AUClast (IV dosing)  interval #> 43                                       AUCall (IV dosing)  interval #> 44                                  AUCint,last (IV dosing)  interval #> 45                                   AUCint,all (IV dosing)  interval #> 46                               AUCbext (based on AUClast)  interval #> 47                                AUCbext (based on AUCall)  interval #> 48                           AUCbext (based on AUCint,last)  interval #> 49                            AUCbext (based on AUCint,all)  interval #> 50                                                Half-life  interval #> 51                                                    $r^2$  interval #> 52                                              $r^2_{adj}$  interval #> 53                                             $\\\\lambda_z$  interval #> 54                              First time for $\\\\lambda_z$  interval #> 55                       Number of points used for lambda_z  interval #> 56                                               Clast,pred  interval #> 57                                               Span ratio  interval #> 58                  Effective half-life (based on MRT,last)  interval #> 59    Effective half-life (for IV dosing, based on MRTlast)  interval #> 60                                   Kel (based on AUClast)  interval #> 61                    Kel (for IV dosing, based on AUClast)  interval #> 62                                               AUCinf,obs  interval #> 63                                              AUCinf,pred  interval #> 64                                             AUMC,inf,obs  interval #> 65                                            AUMC,inf,pred  interval #> 66               AUCint (based on AUCinf,obs extrapolation)  interval #> 67   AUCint (based on AUCinf,obs extrapolation, dose-aware)  interval #> 68              AUCint (based on AUCinf,pred extrapolation)  interval #> 69  AUCint (based on AUCinf,pred extrapolation, dose-aware)  interval #> 70                                   AUCinf,obs (IV dosing)  interval #> 71                                  AUCinf,pred (IV dosing)  interval #> 72                            AUCbext (based on AUCinf,obs)  interval #> 73                           AUCbext (based on AUCinf,pred)  interval #> 74                            AUCpext (based on AUCinf,obs)  interval #> 75                           AUCpext (based on AUCinf,pred)  interval #> 76                                 CL (based on AUCinf,obs)  interval #> 77                                CL (based on AUCinf,pred)  interval #> 78                                MRT (based on AUCinf,obs)  interval #> 79                               MRT (based on AUCinf,pred)  interval #> 80                 MRT (for IV dosing, based on AUCinf,obs)  interval #> 81                MRT (for IV dosing, based on AUCinf,pred)  interval #> 82           MRT (for multiple dosing, based on AUCinf,obs)  interval #> 83          MRT (for multiple dosing, based on AUCinf,pred)  interval #> 84                                 Vz (based on AUCinf,obs)  interval #> 85                                Vz (based on AUCinf,pred)  interval #> 86                                Vss (based on AUCinf,obs)  interval #> 87                               Vss (based on AUCinf,pred)  interval #> 88                 Vss (for IV dosing, based on AUCinf,obs)  interval #> 89                Vss (for IV dosing, based on AUCinf,pred)  interval #> 90              Vss (for multiple-dose, based on Clast,obs)  interval #> 91             Vss (for multiple-dose, based on Clast,pred)  interval #> 92                                  Vd (based on Clast,obs)  interval #> 93                                 Vd (based on Clast,pred)  interval #> 94                   Effective half-life (based on MRT,obs)  interval #> 95                  Effective half-life (based on MRT,pred)  interval #> 96    Effective half-life (for IV dosing, based on MRT,obs)  interval #> 97   Effective half-life (for IV dosing, based on MRT,pred)  interval #> 98                                Kel (based on AUCinf,obs)  interval #> 99                               Kel (based on AUCinf,pred)  interval #> 100                Kel (for IV dosing, based on AUCinf,obs)  interval #> 101               Kel (for IV dosing, based on AUCinf,pred)  interval #> 102                               AUClast (dose-normalized)  interval #> 103                                AUCall (dose-normalized)  interval #> 104                            AUCinf,obs (dose-normalized)  interval #> 105                           AUCinf,pred (dose-normalized)  interval #> 106                             AUMC,last (dose-normalized)  interval #> 107                              AUMC,all (dose-normalized)  interval #> 108                          AUMC,inf,obs (dose-normalized)  interval #> 109                         AUMC,inf,pred (dose-normalized)  interval #> 110                                  Cmax (dose-normalized)  interval #> 111                                  Cmin (dose-normalized)  interval #> 112                                 Clast (dose-normalized)  interval #> 113                            Clast,pred (dose-normalized)  interval #> 114                                   Cav (dose-normalized)  interval #> 115                               Ctrough (dose-normalized)  interval #>                                                                                                                                                                                                                                                                                                                                                              desc #> 1                                                                                                                                                                                                                                                                                                                                   Starting time of the interval #> 2                                                                                                                                                                                                                                                                                                              Ending time of the interval (potentially infinity) #> 3                                                                                                                                                                                                                      The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 4                                                                                                     The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 5                                                                                                                                                                                                               The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification #> 6                                                                                The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification plus the moment of the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 7                                                                                                                                                                                                                                  The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 8                                                                                                                                                                    The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 9                                                                                                                                                                           The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 10                                                                                                            The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 11                                                                                                                                                                                                                                                                                                                        Initial concentration after an IV bolus #> 12                                                                                                                                                                                                                                                                                                                                 Maximum observed concentration #> 13                                                                                                                                                                                                                                                                                                                                 Minimum observed concentration #> 14                                                                                                                                                                                                                                                                                                                     Time of the maximum observed concentration #> 15                                                                                                                                                                                                                                                                                      Time of the last concentration observed above the limit of quantification #> 16                                                                                                                                                                                                                                                                                              Time of the first concentration above the limit of quantification #> 17                                                                                                                                                                                                                                                                                              The last concentration observed above the limit of quantification #> 18                                                                                                                                                                                                                                                                                                       Clearance or observed oral clearance calculated to Clast #> 19                                                                                                                                                                                                                                                                                                    Clearance or observed oral clearance calculated with AUCall #> 20                                                                                                                                                                                                                                                                                                                    Bioavailability or relative bioavailability #> 21                                                                                                                                                                                                                                                                                       The mean residence time to the last observed concentration above the LOQ #> 22                                                                                                                                                                                                                                                        The mean residence time to the last observed concentration above the LOQ correcting for dosing duration #> 23                                                                                                                                                                                                                                                                                              The steady-state volume of distribution calculating through Tlast #> 24                                                                                                                                                                                                                                                                    The steady-state volume of distribution with intravenous infusion calculating through Tlast #> 25                                                                                                                                                                                                                                                                                                                   The average concentration during an interval #> 26                                                                                                                                                                                                                                                                                                                     The trough (end of interval) concentration #> 27                                                                                                                                                                                                                                                                                                                                      The predose concentration #> 28                                                                                                                                                                                                                                                                                                                                Peak-to-Trough ratio (fraction) #> 29                                                                                                                                                                                                                                                                                                                                                       Lag time #> 30                                                                                                                                                                                                                                                                                                                                          Degree of fluctuation #> 31                                                                                                                                                                                                                                                                                                                                         Swing relative to Cmin #> 32                                                                                                                                                                                                                                                                                                                           Concentration at the end of infusion #> 33  The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero #> 34  The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero #> 35                                                                                                                                                                                                                                                                                                            The amount excreted (typically into urine or feces) #> 36                                                                                                                                                                                                                                                                                                                   The renal clearance calculated using AUClast #> 37                                                                                                                                                                                                                                                                                                                The renal clearance calculated using AUCinf,obs #> 38                                                                                                                                                                                                                                                                                                               The renal clearance calculated using AUCinf,pred #> 39                                                                                                                                                                                                                                                                                                                              The fraction of the dose excreted #> 40                                                                                                                                                                                             For sparse PK sampling, the area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 41                                                                                                                                                                                                                                                                                                                               Time above a given concentration #> 42                                                                                                                                                                                                                                                                    The AUClast calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 43                                                                                                                                                                                                                                                                     The AUCall calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 44                                                                                                                                                                                                                                                                The AUCint,last calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 45                                                                                                                                                                                                                                                                 The AUCint,all calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 46                                                                                                                                                                                                                                                                                         The back-extrapolation percent for intravenous dosing based on AUClast #> 47                                                                                                                                                                                                                                                                                          The back-extrapolation percent for intravenous dosing based on AUCall #> 48                                                                                                                                                                                                                                                                                     The back-extrapolation percent for intravenous dosing based on AUCint,last #> 49                                                                                                                                                                                                                                                                                      The back-extrapolation percent for intravenous dosing based on AUCint,all #> 50                                                                                                                                                                                                                                                                                                                                       The (terminal) half-life #> 51                                                                                                                                                                                                                                                                                                                     The r^2 value of the half-life calculation #> 52                                                                                                                                                                                                                                                                                                            The adjusted r^2 value of the half-life calculation #> 53                                                                                                                                                                                                                                                                                                                 The elimination rate of the terminal half-life #> 54                                                                                                                                                                                                                                                                                                     The first time point used for the calculation of half-life #> 55                                                                                                                                                                                                                                                                                                     The number of points used for the calculation of half-life #> 56                                                                                                                                                                                                                                                                                                       The concentration at Tlast as predicted by the half-life #> 57                                                                                                                                                                                                                                                                                      The ratio of the half-life to the duration used for half-life calculation #> 58                                                                                                                                                                                                                                                                                                       The effective half-life (as determined from the MRTlast) #> 59                                                                                                                                                                                                                                                                                           The effective half-life (as determined from the intravenous MRTlast) #> 60                                                                                                                                                                                                                                                                                                    Elimination rate (as calculated from the MRT using AUClast) #> 61                                                                                                                                                                                                                                                                                                  Elimination rate (as calculated from the intravenous MRTlast) #> 62                                                                                                                                                                                                              The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 63                                                                                                                                                                                                             The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast #> 64                                                                                                                                                                                                       The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 65                                                                                                                                                                                                      The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast #> 66                                                                                                                                                                                                                                 The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 67                                                                                                                                                                   The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 68                                                                                                                                                                          The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 69                                                                                                            The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 70                                                                                                                                                                                                                                                                 The AUCinf,obs calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 71                                                                                                                                                                                                                                                                           The  calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 72                                                                                                                                                                                                                                                                                      The back-extrapolation percent for intravenous dosing based on AUCinf,obs #> 73                                                                                                                                                                                                                                                                                     The back-extrapolation percent for intravenous dosing based on AUCinf,pred #> 74                                                                                                                                                                                                                                                                      Percent of the AUCinf that is extrapolated after Tlast calculated from the observed Clast #> 75                                                                                                                                                                                                                                                                     Percent of the AUCinf that is extrapolated after Tlast calculated from the predicted Clast #> 76                                                                                                                                                                                                                                                                                            Clearance or observed oral clearance calculated with observed Clast #> 77                                                                                                                                                                                                                                                                                           Clearance or observed oral clearance calculated with predicted Clast #> 78                                                                                                                                                                                                                                                                                                       The mean residence time to infinity using observed Clast #> 79                                                                                                                                                                                                                                                                                                      The mean residence time to infinity using predicted Clast #> 80                                                                                                                                                                                                                                                                        The mean residence time to infinity using observed Clast correcting for dosing duration #> 81                                                                                                                                                                                                                                                                       The mean residence time to infinity using predicted Clast correcting for dosing duration #> 82                                                                                                                                                                                                                                                                       The mean residence time with multiple dosing and nonlinear kinetics using observed Clast #> 83                                                                                                                                                                                                                                                                      The mean residence time with multiple dosing and nonlinear kinetics using predicted Clast #> 84                                                                                                                                                                                                                                                                                                       The terminal volume of distribution using observed Clast #> 85                                                                                                                                                                                                                                                                                                      The terminal volume of distribution using predicted Clast #> 86                                                                                                                                                                                                                                                                                                   The steady-state volume of distribution using observed Clast #> 87                                                                                                                                                                                                                                                                                                  The steady-state volume of distribution using predicted Clast #> 88                                                                                                                                                                                                                                                                         The steady-state volume of distribution with intravenous infusion using observed Clast #> 89                                                                                                                                                                                                                                                                        The steady-state volume of distribution with intravenous infusion using predicted Clast #> 90                                                                                                                                                                                                                                                                  The steady-state volume of distribution for nonlinear multiple-dose data using observed Clast #> 91                                                                                                                                                                                                                                                                 The steady-state volume of distribution for nonlinear multiple-dose data using predicted Clast #> 92                                                                                                                                                                                                                                                                                        Apparent observed volume of distribution calculated with observed Clast #> 93                                                                                                                                                                                                                                                                                       Apparent observed volume of distribution calculated with predicted Clast #> 94                                                                                                                                                                                                                                                                                                        The effective half-life (as determined from the MRTobs) #> 95                                                                                                                                                                                                                                                                                                       The effective half-life (as determined from the MRTpred) #> 96                                                                                                                                                                                                                                                                                            The effective half-life (as determined from the intravenous MRTobs) #> 97                                                                                                                                                                                                                                                                                           The effective half-life (as determined from the intravenous MRTpred) #> 98                                                                                                                                                                                                                                                                                              Elimination rate (as calculated from the MRT with observed Clast) #> 99                                                                                                                                                                                                                                                                                             Elimination rate (as calculated from the MRT with predicted Clast) #> 100                                                                                                                                                                                                                                                                                                  Elimination rate (as calculated from the intravenous MRTobs) #> 101                                                                                                                                                                                                                                                                                                 Elimination rate (as calculated from the intravenous MRTpred) #> 102                                                                                                                                                                                                                                                                                                                                       Dose normalized auclast #> 103                                                                                                                                                                                                                                                                                                                                        Dose normalized aucall #> 104                                                                                                                                                                                                                                                                                                                                    Dose normalized aucinf.obs #> 105                                                                                                                                                                                                                                                                                                                                   Dose normalized aucinf.pred #> 106                                                                                                                                                                                                                                                                                                                                      Dose normalized aumclast #> 107                                                                                                                                                                                                                                                                                                                                       Dose normalized aumcall #> 108                                                                                                                                                                                                                                                                                                                                   Dose normalized aumcinf.obs #> 109                                                                                                                                                                                                                                                                                                                                  Dose normalized aumcinf.pred #> 110                                                                                                                                                                                                                                                                                                                                          Dose normalized cmax #> 111                                                                                                                                                                                                                                                                                                                                          Dose normalized cmin #> 112                                                                                                                                                                                                                                                                                                                                     Dose normalized clast.obs #> 113                                                                                                                                                                                                                                                                                                                                    Dose normalized clast.pred #> 114                                                                                                                                                                                                                                                                                                                                           Dose normalized cav #> 115                                                                                                                                                                                                                                                                                                                                       Dose normalized ctrough #>"},{"path":"/reference/NCA_fetch_ana_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Analysis Dataset — NCA_fetch_ana_ds","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"Fetches dataset used specified analysis","code":""},{"path":"/reference/NCA_fetch_ana_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"","code":"NCA_fetch_ana_ds(state, current_ana)"},{"path":"/reference/NCA_fetch_ana_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"state NCA state NCA_fetch_state() current_ana Current value analysis","code":""},{"path":"/reference/NCA_fetch_ana_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"Dataset ds field FM_fetch_ds()","code":""},{"path":"/reference/NCA_fetch_ana_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:8d3ce4e34528cd358d3211b5f3d1fa32                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_ana_pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"Fetches PKNCA output specified analysis","code":""},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"","code":"NCA_fetch_ana_pknca(state, current_ana)"},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"state NCA state NCA_fetch_state() current_ana Current value analysis","code":""},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"Dataset ds field FM_fetch_ds()","code":""},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:9969a022af47a682c761107bf7822b7e                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Code — NCA_fetch_code","title":"Fetch Module Code — NCA_fetch_code","text":"Fetches code generate results seen app","code":""},{"path":"/reference/NCA_fetch_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Code — NCA_fetch_code","text":"","code":"NCA_fetch_code(state)"},{"path":"/reference/NCA_fetch_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Code — NCA_fetch_code","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_fetch_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Code — NCA_fetch_code","text":"Character object vector lines code","code":""},{"path":"/reference/NCA_fetch_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Code — NCA_fetch_code","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:12189e9d19ef3a975d19c5f92e25266b                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_current_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Current Analysis — NCA_fetch_current_ana","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"Takes NCA state returns current active analysis","code":""},{"path":"/reference/NCA_fetch_current_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"","code":"NCA_fetch_current_ana(state)"},{"path":"/reference/NCA_fetch_current_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_fetch_current_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"List containing details current analysis. structure list structure state$NCA$anas output NCA_fetch_state().","code":""},{"path":"/reference/NCA_fetch_current_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:417faffb9dc4318f681da675cb692e45                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_current_obj.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches the Current Analysis Object — NCA_fetch_current_obj","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"Takes current state object type returns currently selected object. example specified figure, look output figure selected figure number figure return ggplot object . subject id highlighting certain NCA aspects (e.g. points used half-life)","code":""},{"path":"/reference/NCA_fetch_current_obj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"","code":"NCA_fetch_current_obj(state, obj_type)"},{"path":"/reference/NCA_fetch_current_obj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"state NCA state NCA_fetch_state() obj_type Type object return (either \"table\" \"figure\").","code":""},{"path":"/reference/NCA_fetch_current_obj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"List format depends obj_type. figures: ggplot:   ggplot object figure. isgood:   Return status function. msgs:     Messages passed back user. tables: df:       Dataframe current table. ft:       Flextable object current table. notes:    table notes included. isgood:   Return status function. msgs:     Messages passed back user.","code":""},{"path":"/reference/NCA_fetch_current_obj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"Time Concentration (ng/mL) ID (day) 1 2 3 0 BLQ BLQ BLQ 1 690 750 670 2 588 577 576 3 506 508 501 4 438 476 441 7 303 425 320 21 123 274 139 28 95.5 220 104 35 75.8 177 77.8 42 60.6 142 58.6","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:6b27b5c20b9e22136fca1148a812ea1f state = sess_res$state  # Current active table: res = NCA_fetch_current_obj(state, \"table\") res$ft .cl-3aff2164{}.cl-3afadf3c{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-3afd1d56{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-3afd283c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd283d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2846{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2847{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2848{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2850{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2851{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd2852{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285b{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3afd285d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}TimeConcentration (ng/mL) by ID(day)1230BLQBLQBLQ16907506702588577576350650850144384764417303425320211232741392895.52201043575.817777.84260.614258.6 # Current active figure: res = NCA_fetch_current_obj(state, \"figure\") res$ggplot"},{"path":"/reference/NCA_fetch_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Details About Data Requirements — NCA_fetch_data_format","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"Use get information data formats.","code":""},{"path":"/reference/NCA_fetch_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"","code":"NCA_fetch_data_format(   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\") )"},{"path":"/reference/NCA_fetch_data_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"MOD_yaml_file Module configuration file MC main section.","code":""},{"path":"/reference/NCA_fetch_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"List details data formats","code":""},{"path":"/reference/NCA_fetch_data_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"","code":"NCA_fetch_data_format() #>   sname              Column Required #> 1    id          Subject ID      yes #> 2  time                Time      yes #> 3 ntime        Nominal Time      yes #> 4  conc       Concentration      yes #> 5  dose                Dose      yes #> 6 route               Route      yes #> 7   dur Duration (optional)       no #> 8 cycle          Dose cycle      yes #>                                                                                                                          Description #> 1                                                                                                         Unique subject identifier. #> 2                                                                                        Time since the first dose. Must be numeric. #> 3                                                       Time since the last dose in the cycle (same units as Time). Must be numeric. #> 4                                          Observed concentration. Must be numeric, no NA values. Missing values should be set to 0. #> 5                                                                                             Value of dose given (must be numeric). #> 6 Doseing route. It should be either 'extravascular' or 'intravascular' but ruminate will make an effort to figure out other values. #> 7                                                 Doseing duration (same units as Time). If set to N/A a value of 0 will be assumed. #> 8                                                                                                                 Current dose cycle"},{"path":"/reference/NCA_fetch_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Datasets — NCA_fetch_ds","title":"Fetch Module Datasets — NCA_fetch_ds","text":"Fetches datasets contained module","code":""},{"path":"/reference/NCA_fetch_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Datasets — NCA_fetch_ds","text":"","code":"NCA_fetch_ds(state)"},{"path":"/reference/NCA_fetch_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Datasets — NCA_fetch_ds","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_fetch_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Datasets — NCA_fetch_ds","text":"list containing following elements isgood:    Return status function. hasds:     Boolean indicator module datasets msgs:      Messages passed back user. ds:        List datasets. list element name R-object dataset. element following structure: label: Text label dataset MOD_TYPE: Short name type module. id: module ID DS: Dataframe containing actual dataset. DSMETA: Metadata describing DS code: Complete code build dataset. checksum: Module checksum. DSchecksum: Dataset checksum.","code":""},{"path":"/reference/NCA_fetch_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Datasets — NCA_fetch_ds","text":"","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:5ccca71fdb91ecdaf857355530e8c73a state = sess_res$state  myDs = NCA_fetch_ds(state)"},{"path":"/reference/NCA_fetch_np_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"provides meta information NCA parameters. includes parameter names, text descriptions, formatting (md LaTeX).","code":""},{"path":"/reference/NCA_fetch_np_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"","code":"NCA_fetch_np_meta(   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\") )"},{"path":"/reference/NCA_fetch_np_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"MOD_yaml_file Module configuration file MC main section.","code":""},{"path":"/reference/NCA_fetch_np_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"List following elements: choices: List parameter choices grouped values specified module configuration file. summary: Data frame meta data NCA parameters following columns: parameter:   Name parameter PKNCA. text:        Name parameter plain text. md:          Parameter name formatted Markdown. latex:       Parameter name formatted using LaTeX. description: Verbose description plain text parameter.","code":""},{"path":"/reference/NCA_fetch_np_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"","code":"NCA_fetch_np_meta() #> $choices #> $choices$`Common Parameters` #> $choices$`Common Parameters`$AUCall #> [1] \"aucall\" #>  #> $choices$`Common Parameters`$C0 #> [1] \"c0\" #>  #> $choices$`Common Parameters`$`CL (based on AUCall)` #> [1] \"cl.all\" #>  #> $choices$`Common Parameters`$`CL (based on AUClast)` #> [1] \"cl.last\" #>  #> $choices$`Common Parameters`$Cmax #> [1] \"cmax\" #>  #> $choices$`Common Parameters`$`Half-life` #> [1] \"half.life\" #>  #> $choices$`Common Parameters`$`r²` #> [1] \"r.squared\" #>  #> $choices$`Common Parameters`$`Sparse AUClast` #> [1] \"sparse_auclast\" #>  #>  #> $choices$Other #> $choices$Other$`r² (Adj)` #> [1] \"adj.r.squared\" #>  #> $choices$Other$`Amount excreted` #> [1] \"ae\" #>  #> $choices$Other$`AUCall (dose-normalized)` #> [1] \"aucall.dn\" #>  #> $choices$Other$`AUCinf,obs` #> [1] \"aucinf.obs\" #>  #> $choices$Other$`AUCinf,obs (dose-normalized)` #> [1] \"aucinf.obs.dn\" #>  #> $choices$Other$`AUCinf,pred (dose-normalized)` #> [1] \"aucinf.pred.dn\" #>  #> $choices$Other$`AUCint (based on AUCall extrapolation)` #> [1] \"aucint.all\" #>  #> $choices$Other$`AUCint (based on AUCall extrapolation, dose-aware)` #> [1] \"aucint.all.dose\" #>  #> $choices$Other$`AUCint (based on AUCinf,obs extrapolation)` #> [1] \"aucint.inf.obs\" #>  #> $choices$Other$`AUCint (based on AUCinf,obs extrapolation, dose-aware)` #> [1] \"aucint.inf.obs.dose\" #>  #> $choices$Other$`AUCint (based on AUCinf,pred extrapolation)` #> [1] \"aucint.inf.pred\" #>  #> $choices$Other$`AUCint (based on AUCinf,pred extrapolation, dose-aware)` #> [1] \"aucint.inf.pred.dose\" #>  #> $choices$Other$`AUCint (based on AUClast extrapolation)` #> [1] \"aucint.last\" #>  #> $choices$Other$`AUCint (based on AUClast extrapolation, dose-aware)` #> [1] \"aucint.last.dose\" #>  #> $choices$Other$`AUCall (IV dosing)` #> [1] \"aucivall\" #>  #> $choices$Other$`AUCinf,obs (IV dosing)` #> [1] \"aucivinf.obs\" #>  #> $choices$Other$`AUCinf,pred (IV dosing)` #> [1] \"aucivinf.pred\" #>  #> $choices$Other$`AUCint,all (IV dosing)` #> [1] \"aucivint.all\" #>  #> $choices$Other$`AUCint,last (IV dosing)` #> [1] \"aucivint.last\" #>  #> $choices$Other$`AUClast (IV dosing)` #> [1] \"aucivlast\" #>  #> $choices$Other$`AUCbext (based on AUCall)` #> [1] \"aucivpbextall\" #>  #> $choices$Other$`AUCbext (based on AUCinf,obs)` #> [1] \"aucivpbextinf.obs\" #>  #> $choices$Other$`AUCbext (based on AUCinf,pred)` #> [1] \"aucivpbextinf.pred\" #>  #> $choices$Other$`AUCbext (based on AUCint,all)` #> [1] \"aucivpbextint.all\" #>  #> $choices$Other$`AUCbext (based on AUCint,last)` #> [1] \"aucivpbextint.last\" #>  #> $choices$Other$`AUCbext (based on AUClast)` #> [1] \"aucivpbextlast\" #>  #> $choices$Other$AUClast #> [1] \"auclast\" #>  #> $choices$Other$`AUClast (dose-normalized)` #> [1] \"auclast.dn\" #>  #> $choices$Other$`AUCpext (based on AUCinf,obs)` #> [1] \"aucpext.obs\" #>  #> $choices$Other$`AUCpext (based on AUCinf,pred)` #> [1] \"aucpext.pred\" #>  #> $choices$Other$`AUMC,all` #> [1] \"aumcall\" #>  #> $choices$Other$`AUMC,all (dose-normalized)` #> [1] \"aumcall.dn\" #>  #> $choices$Other$`AUMC,inf,obs` #> [1] \"aumcinf.obs\" #>  #> $choices$Other$`AUMC,inf,obs (dose-normalized)` #> [1] \"aumcinf.obs.dn\" #>  #> $choices$Other$`AUMC,inf,pred (dose-normalized)` #> [1] \"aumcinf.pred.dn\" #>  #> $choices$Other$`AUMC,last` #> [1] \"aumclast\" #>  #> $choices$Other$`AUMC,last (dose-normalized)` #> [1] \"aumclast.dn\" #>  #> $choices$Other$Cav #> [1] \"cav\" #>  #> $choices$Other$`Cav (dose-normalized)` #> [1] \"cav.dn\" #>  #> $choices$Other$Ceoi #> [1] \"ceoi\" #>  #> $choices$Other$`CL (based on AUCinf,obs)` #> [1] \"cl.obs\" #>  #> $choices$Other$`CL (based on AUCinf,pred)` #> [1] \"cl.pred\" #>  #> $choices$Other$Clast #> [1] \"clast.obs\" #>  #> $choices$Other$`Clast,pred` #> [1] \"clast.pred\" #>  #> $choices$Other$`Clast,pred (dose-normalized)` #> [1] \"clast.pred.dn\" #>  #> $choices$Other$`Renal clearance (from AUClast)` #> [1] \"clr.last\" #>  #> $choices$Other$`Renal clearance (from AUCinf,obs)` #> [1] \"clr.obs\" #>  #> $choices$Other$`Renal clearance (from AUCinf,pred)` #> [1] \"clr.pred\" #>  #> $choices$Other$`Cmax (dose-normalized)` #> [1] \"cmax.dn\" #>  #> $choices$Other$Cmin #> [1] \"cmin\" #>  #> $choices$Other$`Cmin (dose-normalized)` #> [1] \"cmin.dn\" #>  #> $choices$Other$Ctrough #> [1] \"ctrough\" #>  #> $choices$Other$`Ctrough (dose-normalized)` #> [1] \"ctrough.dn\" #>  #> $choices$Other$`Degree of fluctuation` #> [1] \"deg.fluc\" #>  #> $choices$Other$`Interval End` #> [1] \"end\" #>  #> $choices$Other$Bioavailability #> [1] \"f\" #>  #> $choices$Other$`Fraction excreted` #> [1] \"fe\" #>  #> $choices$Other$`Kel (for IV dosing, based on AUClast)` #> [1] \"kel.iv.last\" #>  #> $choices$Other$`Kel (for IV dosing, based on AUCinf,obs)` #> [1] \"kel.iv.obs\" #>  #> $choices$Other$`Kel (for IV dosing, based on AUCinf,pred)` #> [1] \"kel.iv.pred\" #>  #> $choices$Other$`Kel (based on AUClast)` #> [1] \"kel.last\" #>  #> $choices$Other$`Kel (based on AUCinf,obs)` #> [1] \"kel.obs\" #>  #> $choices$Other$`Kel (based on AUCinf,pred)` #> [1] \"kel.pred\" #>  #> $choices$Other$λz #> [1] \"lambda.z\" #>  #> $choices$Other$`λz,n` #> [1] \"lambda.z.n.points\" #>  #> $choices$Other$`λz (t 1ˢᵗ)` #> [1] \"lambda.z.time.first\" #>  #> $choices$Other$`MRT (for IV dosing, based on AUClast)` #> [1] \"mrt.iv.last\" #>  #> $choices$Other$`MRT (for IV dosing, based on AUCinf,obs)` #> [1] \"mrt.iv.obs\" #>  #> $choices$Other$`MRT (for IV dosing, based on AUCinf,pred)` #> [1] \"mrt.iv.pred\" #>  #> $choices$Other$`MRT (based on AUClast)` #> [1] \"mrt.last\" #>  #> $choices$Other$`MRT (for multiple dosing, based on AUCinf,obs)` #> [1] \"mrt.md.obs\" #>  #> $choices$Other$`MRT (for multiple dosing, based on AUCinf,pred)` #> [1] \"mrt.md.pred\" #>  #> $choices$Other$`MRT (based on AUCinf,obs)` #> [1] \"mrt.obs\" #>  #> $choices$Other$`MRT (based on AUCinf,pred)` #> [1] \"mrt.pred\" #>  #> $choices$Other$`Peak-to-trough ratio` #> [1] \"ptr\" #>  #> $choices$Other$`Span ratio` #> [1] \"span.ratio\" #>  #> $choices$Other$`Interval Start` #> [1] \"start\" #>  #> $choices$Other$Swing #> [1] \"swing\" #>  #> $choices$Other$`t 1ˢᵗ` #> [1] \"tfirst\" #>  #> $choices$Other$`Effective half-life (for IV dosing, based on MRTlast)` #> [1] \"thalf.eff.iv.last\" #>  #> $choices$Other$`Effective half-life (for IV dosing, based on MRT,obs)` #> [1] \"thalf.eff.iv.obs\" #>  #> $choices$Other$`Effective half-life (for IV dosing, based on MRT,pred)` #> [1] \"thalf.eff.iv.pred\" #>  #> $choices$Other$`Effective half-life (based on MRT,last)` #> [1] \"thalf.eff.last\" #>  #> $choices$Other$`Effective half-life (based on MRT,obs)` #> [1] \"thalf.eff.obs\" #>  #> $choices$Other$`Effective half-life (based on MRT,pred)` #> [1] \"thalf.eff.pred\" #>  #> $choices$Other$`Time above Concentration` #> [1] \"time_above\" #>  #> $choices$Other$Tlag #> [1] \"tlag\" #>  #> $choices$Other$Tlast #> [1] \"tlast\" #>  #> $choices$Other$`Vd (based on Clast,obs)` #> [1] \"vd.obs\" #>  #> $choices$Other$`Vd (based on Clast,pred)` #> [1] \"vd.pred\" #>  #> $choices$Other$`Vss (for IV dosing, based on AUClast)` #> [1] \"vss.iv.last\" #>  #> $choices$Other$`Vss (for IV dosing, based on AUCinf,obs)` #> [1] \"vss.iv.obs\" #>  #> $choices$Other$`Vss (for IV dosing, based on AUCinf,pred)` #> [1] \"vss.iv.pred\" #>  #> $choices$Other$`Vss (based on AUClast)` #> [1] \"vss.last\" #>  #> $choices$Other$`Vss (for multiple-dose, based on Clast,obs)` #> [1] \"vss.md.obs\" #>  #> $choices$Other$`Vss (for multiple-dose, based on Clast,pred)` #> [1] \"vss.md.pred\" #>  #> $choices$Other$`Vss (based on AUCinf,obs)` #> [1] \"vss.obs\" #>  #> $choices$Other$`Vss (based on AUCinf,pred)` #> [1] \"vss.pred\" #>  #> $choices$Other$`Vz (based on AUCinf,obs)` #> [1] \"vz.obs\" #>  #> $choices$Other$`Vz (based on AUCinf,pred)` #> [1] \"vz.pred\" #>  #>  #>  #> $summary #>                parameter #> 1                 aucall #> 2          adj.r.squared #> 3                     ae #> 4              aucall.dn #> 5             aucinf.obs #> 6          aucinf.obs.dn #> 7         aucinf.pred.dn #> 8             aucint.all #> 9        aucint.all.dose #> 10        aucint.inf.obs #> 11   aucint.inf.obs.dose #> 12       aucint.inf.pred #> 13  aucint.inf.pred.dose #> 14           aucint.last #> 15      aucint.last.dose #> 16              aucivall #> 17          aucivinf.obs #> 18         aucivinf.pred #> 19          aucivint.all #> 20         aucivint.last #> 21             aucivlast #> 22         aucivpbextall #> 23     aucivpbextinf.obs #> 24    aucivpbextinf.pred #> 25     aucivpbextint.all #> 26    aucivpbextint.last #> 27        aucivpbextlast #> 28               auclast #> 29            auclast.dn #> 30           aucpext.obs #> 31          aucpext.pred #> 32               aumcall #> 33            aumcall.dn #> 34           aumcinf.obs #> 35        aumcinf.obs.dn #> 36       aumcinf.pred.dn #> 37              aumclast #> 38           aumclast.dn #> 39                    c0 #> 40                   cav #> 41                cav.dn #> 42                  ceoi #> 43                cl.all #> 44               cl.last #> 45                cl.obs #> 46               cl.pred #> 47             clast.obs #> 48            clast.pred #> 49         clast.pred.dn #> 50              clr.last #> 51               clr.obs #> 52              clr.pred #> 53                  cmax #> 54               cmax.dn #> 55                  cmin #> 56               cmin.dn #> 57               ctrough #> 58            ctrough.dn #> 59              deg.fluc #> 60                   end #> 61                     f #> 62                    fe #> 63             half.life #> 64           kel.iv.last #> 65            kel.iv.obs #> 66           kel.iv.pred #> 67              kel.last #> 68               kel.obs #> 69              kel.pred #> 70              lambda.z #> 71     lambda.z.n.points #> 72   lambda.z.time.first #> 73           mrt.iv.last #> 74            mrt.iv.obs #> 75           mrt.iv.pred #> 76              mrt.last #> 77            mrt.md.obs #> 78           mrt.md.pred #> 79               mrt.obs #> 80              mrt.pred #> 81                   ptr #> 82             r.squared #> 83            span.ratio #> 84        sparse_auclast #> 85                 start #> 86                 swing #> 87                tfirst #> 88     thalf.eff.iv.last #> 89      thalf.eff.iv.obs #> 90     thalf.eff.iv.pred #> 91        thalf.eff.last #> 92         thalf.eff.obs #> 93        thalf.eff.pred #> 94            time_above #> 95                  tlag #> 96                 tlast #> 97                vd.obs #> 98               vd.pred #> 99           vss.iv.last #> 100           vss.iv.obs #> 101          vss.iv.pred #> 102             vss.last #> 103           vss.md.obs #> 104          vss.md.pred #> 105              vss.obs #> 106             vss.pred #> 107               vz.obs #> 108              vz.pred #>                                                        text #> 1                                                    AUCall #> 2                                                  r² (Adj) #> 3                                           Amount excreted #> 4                                  AUCall (dose-normalized) #> 5                                                AUCinf,obs #> 6                              AUCinf,obs (dose-normalized) #> 7                             AUCinf,pred (dose-normalized) #> 8                    AUCint (based on AUCall extrapolation) #> 9        AUCint (based on AUCall extrapolation, dose-aware) #> 10               AUCint (based on AUCinf,obs extrapolation) #> 11   AUCint (based on AUCinf,obs extrapolation, dose-aware) #> 12              AUCint (based on AUCinf,pred extrapolation) #> 13  AUCint (based on AUCinf,pred extrapolation, dose-aware) #> 14                  AUCint (based on AUClast extrapolation) #> 15      AUCint (based on AUClast extrapolation, dose-aware) #> 16                                       AUCall (IV dosing) #> 17                                   AUCinf,obs (IV dosing) #> 18                                  AUCinf,pred (IV dosing) #> 19                                   AUCint,all (IV dosing) #> 20                                  AUCint,last (IV dosing) #> 21                                      AUClast (IV dosing) #> 22                                AUCbext (based on AUCall) #> 23                            AUCbext (based on AUCinf,obs) #> 24                           AUCbext (based on AUCinf,pred) #> 25                            AUCbext (based on AUCint,all) #> 26                           AUCbext (based on AUCint,last) #> 27                               AUCbext (based on AUClast) #> 28                                                  AUClast #> 29                                AUClast (dose-normalized) #> 30                            AUCpext (based on AUCinf,obs) #> 31                           AUCpext (based on AUCinf,pred) #> 32                                                 AUMC,all #> 33                               AUMC,all (dose-normalized) #> 34                                             AUMC,inf,obs #> 35                           AUMC,inf,obs (dose-normalized) #> 36                          AUMC,inf,pred (dose-normalized) #> 37                                                AUMC,last #> 38                              AUMC,last (dose-normalized) #> 39                                                       C0 #> 40                                                      Cav #> 41                                    Cav (dose-normalized) #> 42                                                     Ceoi #> 43                                     CL (based on AUCall) #> 44                                    CL (based on AUClast) #> 45                                 CL (based on AUCinf,obs) #> 46                                CL (based on AUCinf,pred) #> 47                                                    Clast #> 48                                               Clast,pred #> 49                             Clast,pred (dose-normalized) #> 50                           Renal clearance (from AUClast) #> 51                        Renal clearance (from AUCinf,obs) #> 52                       Renal clearance (from AUCinf,pred) #> 53                                                     Cmax #> 54                                   Cmax (dose-normalized) #> 55                                                     Cmin #> 56                                   Cmin (dose-normalized) #> 57                                                  Ctrough #> 58                                Ctrough (dose-normalized) #> 59                                    Degree of fluctuation #> 60                                             Interval End #> 61                                          Bioavailability #> 62                                        Fraction excreted #> 63                                                Half-life #> 64                    Kel (for IV dosing, based on AUClast) #> 65                 Kel (for IV dosing, based on AUCinf,obs) #> 66                Kel (for IV dosing, based on AUCinf,pred) #> 67                                   Kel (based on AUClast) #> 68                                Kel (based on AUCinf,obs) #> 69                               Kel (based on AUCinf,pred) #> 70                                                       λz #> 71                                                     λz,n #> 72                                               λz (t 1ˢᵗ) #> 73                    MRT (for IV dosing, based on AUClast) #> 74                 MRT (for IV dosing, based on AUCinf,obs) #> 75                MRT (for IV dosing, based on AUCinf,pred) #> 76                                   MRT (based on AUClast) #> 77           MRT (for multiple dosing, based on AUCinf,obs) #> 78          MRT (for multiple dosing, based on AUCinf,pred) #> 79                                MRT (based on AUCinf,obs) #> 80                               MRT (based on AUCinf,pred) #> 81                                     Peak-to-trough ratio #> 82                                                       r² #> 83                                               Span ratio #> 84                                           Sparse AUClast #> 85                                           Interval Start #> 86                                                    Swing #> 87                                                    t 1ˢᵗ #> 88    Effective half-life (for IV dosing, based on MRTlast) #> 89    Effective half-life (for IV dosing, based on MRT,obs) #> 90   Effective half-life (for IV dosing, based on MRT,pred) #> 91                  Effective half-life (based on MRT,last) #> 92                   Effective half-life (based on MRT,obs) #> 93                  Effective half-life (based on MRT,pred) #> 94                                 Time above Concentration #> 95                                                     Tlag #> 96                                                    Tlast #> 97                                  Vd (based on Clast,obs) #> 98                                 Vd (based on Clast,pred) #> 99                    Vss (for IV dosing, based on AUClast) #> 100                Vss (for IV dosing, based on AUCinf,obs) #> 101               Vss (for IV dosing, based on AUCinf,pred) #> 102                                  Vss (based on AUClast) #> 103             Vss (for multiple-dose, based on Clast,obs) #> 104            Vss (for multiple-dose, based on Clast,pred) #> 105                               Vss (based on AUCinf,obs) #> 106                              Vss (based on AUCinf,pred) #> 107                                Vz (based on AUCinf,obs) #> 108                               Vz (based on AUCinf,pred) #>                                 md #> 1                         AUC~all~ #> 2                        Adj. R^2^ #> 3                               AE #> 4                      AUC~all,dn~ #> 5                     AUC~inf,obs~ #> 6                  AUC~inf,obs,dn~ #> 7                 AUC~inf,pred,dn~ #> 8                     AUC~int,all~ #> 9                AUC~int,all,dose~ #> 10                AUC~int,inf,obs~ #> 11           AUC~int,inf,obs,dose~ #> 12               AUC~int,inf,pred~ #> 13          AUC~int,inf,pred,dose~ #> 14                   AUC~int,last~ #> 15              AUC~int,last,dose~ #> 16                     AUC~iv,all~ #> 17                 AUC~iv,inf,obs~ #> 18                AUC~iv,inf,pred~ #> 19                 AUC~iv,int,all~ #> 20                AUC~iv,int,last~ #> 21                    AUC~iv,last~ #> 22                 AUC~iv,pbe,all~ #> 23             AUC~iv,pbe,inf,obs~ #> 24            AUC~iv,pbe,inf,pred~ #> 25             AUC~iv,pbe,inf,all~ #> 26            AUC~iv,pbe,inf,last~ #> 27                AUC~iv,pbe,last~ #> 28                       AUC~last~ #> 29                    AUC~last,dn~ #> 30                   AUC~pext,obs~ #> 31                  AUC~pext,pred~ #> 32                       AUMC~all~ #> 33                    AUMC~all,dn~ #> 34                   AUMC~inf,obs~ #> 35                AUMC~inf,obs,dn~ #> 36               AUMC~inf,pred,dn~ #> 37                      AUMC~last~ #> 38                   AUMC~last,dn~ #> 39                            C~0~ #> 40                          C~ave~ #> 41                       C~ave,dn~ #> 42                          C~EOI~ #> 43                              CL #> 44                        CL~last~ #> 45                         CL~obs~ #> 46                        CL~pred~ #> 47                    CL~last,obs~ #> 48                   CL~last,pred~ #> 49                CL~last,pred,dn~ #> 50                      CL~R,last~ #> 51                       CL~R,obs~ #> 52                      CL~R,pred~ #> 53                          C~max~ #> 54                       C~max,dn~ #> 55                          C~min~ #> 56                       C~min,dn~ #> 57                           C~tr~ #> 58                        C~tr,dn~ #> 59                             dgf #> 60                          t~end~ #> 61                            f~b~ #> 62                            f~e~ #> 63                         t~half~ #> 64                   k~el,iv,last~ #> 65                    k~el,iv,obs~ #> 66                   k~el,iv,pred~ #> 67                      k~el,last~ #> 68                       k~el,obs~ #> 69                      k~el,pred~ #> 70            <ff:symbol>l<\/ff>~z~ #> 71          <ff:symbol>l<\/ff>~z,n~ #> 72  <ff:symbol>l<\/ff>~z~ (t 1^st^) #> 73                    MRT~iv,last~ #> 74                     MRT~iv,obs~ #> 75                    MRT~iv,pred~ #> 76                       MRT~last~ #> 77                     MRT~md,obs~ #> 78                    MRT~md,pred~ #> 79                        MRT~obs~ #> 80                       MRT~pred~ #> 81                             PTR #> 82                            R^2^ #> 83                              SR #> 84                    AUC~last,sp~ #> 85                        t~start~ #> 86                           SWING #> 87                        t~first~ #> 88             t~half,eff,iv,last~ #> 89              t~half,eff,iv,obs~ #> 90             t~half,eff,iv,pred~ #> 91                t~half,eff,last~ #> 92                 t~half,eff,obs~ #> 93                t~half,eff,pred~ #> 94                           t~ab~ #> 95                          t~lag~ #> 96                         t~last~ #> 97                        V~d,obs~ #> 98                       V~d,pred~ #> 99                   V~ss,iv,last~ #> 100                   V~ss,iv,obs~ #> 101                  V~ss,iv,pred~ #> 102                  V~ss,iv,last~ #> 103                   V~ss,md,obs~ #> 104                  V~ss,md,pred~ #> 105                      V~ss,obs~ #> 106                     V~ss,pred~ #> 107                       V~z,obs~ #> 108                      V~z,pred~ #>                                                       latex #> 1                                                    AUCall #> 2                                               $r^2_{adj}$ #> 3                                           Amount excreted #> 4                                  AUCall (dose-normalized) #> 5                                                AUCinf,obs #> 6                              AUCinf,obs (dose-normalized) #> 7                             AUCinf,pred (dose-normalized) #> 8                    AUCint (based on AUCall extrapolation) #> 9        AUCint (based on AUCall extrapolation, dose-aware) #> 10               AUCint (based on AUCinf,obs extrapolation) #> 11   AUCint (based on AUCinf,obs extrapolation, dose-aware) #> 12              AUCint (based on AUCinf,pred extrapolation) #> 13  AUCint (based on AUCinf,pred extrapolation, dose-aware) #> 14                  AUCint (based on AUClast extrapolation) #> 15      AUCint (based on AUClast extrapolation, dose-aware) #> 16                                       AUCall (IV dosing) #> 17                                   AUCinf,obs (IV dosing) #> 18                                  AUCinf,pred (IV dosing) #> 19                                   AUCint,all (IV dosing) #> 20                                  AUCint,last (IV dosing) #> 21                                      AUClast (IV dosing) #> 22                                AUCbext (based on AUCall) #> 23                            AUCbext (based on AUCinf,obs) #> 24                           AUCbext (based on AUCinf,pred) #> 25                            AUCbext (based on AUCint,all) #> 26                           AUCbext (based on AUCint,last) #> 27                               AUCbext (based on AUClast) #> 28                                                  AUClast #> 29                                AUClast (dose-normalized) #> 30                            AUCpext (based on AUCinf,obs) #> 31                           AUCpext (based on AUCinf,pred) #> 32                                                 AUMC,all #> 33                               AUMC,all (dose-normalized) #> 34                                             AUMC,inf,obs #> 35                           AUMC,inf,obs (dose-normalized) #> 36                          AUMC,inf,pred (dose-normalized) #> 37                                                AUMC,last #> 38                              AUMC,last (dose-normalized) #> 39                                                       C0 #> 40                                                      Cav #> 41                                    Cav (dose-normalized) #> 42                                                     Ceoi #> 43                                     CL (based on AUCall) #> 44                                    CL (based on AUClast) #> 45                                 CL (based on AUCinf,obs) #> 46                                CL (based on AUCinf,pred) #> 47                                                    Clast #> 48                                               Clast,pred #> 49                             Clast,pred (dose-normalized) #> 50                           Renal clearance (from AUClast) #> 51                        Renal clearance (from AUCinf,obs) #> 52                       Renal clearance (from AUCinf,pred) #> 53                                                     Cmax #> 54                                   Cmax (dose-normalized) #> 55                                                     Cmin #> 56                                   Cmin (dose-normalized) #> 57                                                  Ctrough #> 58                                Ctrough (dose-normalized) #> 59                                    Degree of fluctuation #> 60                                             Interval End #> 61                                          Bioavailability #> 62                                        Fraction excreted #> 63                                                Half-life #> 64                    Kel (for IV dosing, based on AUClast) #> 65                 Kel (for IV dosing, based on AUCinf,obs) #> 66                Kel (for IV dosing, based on AUCinf,pred) #> 67                                   Kel (based on AUClast) #> 68                                Kel (based on AUCinf,obs) #> 69                               Kel (based on AUCinf,pred) #> 70                                             $\\\\lambda_z$ #> 71                       Number of points used for lambda_z #> 72                              First time for $\\\\lambda_z$ #> 73                    MRT (for IV dosing, based on AUClast) #> 74                 MRT (for IV dosing, based on AUCinf,obs) #> 75                MRT (for IV dosing, based on AUCinf,pred) #> 76                                   MRT (based on AUClast) #> 77           MRT (for multiple dosing, based on AUCinf,obs) #> 78          MRT (for multiple dosing, based on AUCinf,pred) #> 79                                MRT (based on AUCinf,obs) #> 80                               MRT (based on AUCinf,pred) #> 81                                     Peak-to-trough ratio #> 82                                                    $r^2$ #> 83                                               Span ratio #> 84                                           Sparse AUClast #> 85                                           Interval Start #> 86                                                    Swing #> 87                                                   Tfirst #> 88    Effective half-life (for IV dosing, based on MRTlast) #> 89    Effective half-life (for IV dosing, based on MRT,obs) #> 90   Effective half-life (for IV dosing, based on MRT,pred) #> 91                  Effective half-life (based on MRT,last) #> 92                   Effective half-life (based on MRT,obs) #> 93                  Effective half-life (based on MRT,pred) #> 94                                 Time above Concentration #> 95                                                     Tlag #> 96                                                    Tlast #> 97                                  Vd (based on Clast,obs) #> 98                                 Vd (based on Clast,pred) #> 99                    Vss (for IV dosing, based on AUClast) #> 100                Vss (for IV dosing, based on AUCinf,obs) #> 101               Vss (for IV dosing, based on AUCinf,pred) #> 102                                  Vss (based on AUClast) #> 103             Vss (for multiple-dose, based on Clast,obs) #> 104            Vss (for multiple-dose, based on Clast,pred) #> 105                               Vss (based on AUCinf,obs) #> 106                              Vss (based on AUCinf,pred) #> 107                                Vz (based on AUCinf,obs) #> 108                               Vz (based on AUCinf,pred) #>                                                                                                                                                                                                                                                                          description #> 1                        The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 2                                                                                                                                                                                                                                The adjusted r^2 value of the half-life calculation #> 3                                                                                                                                                                                                                                The amount excreted (typically into urine or feces) #> 4                                                                                                                                                                                                                                                             Dose normalized aucall #> 5                                                                                                                                  The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 6                                                                                                                                                                                                                                                         Dose normalized aucinf.obs #> 7                                                                                                                                                                                                                                                        Dose normalized aucinf.pred #> 8                                                                                              The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 9                                The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 10                                                                                                                                                    The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 11                                                                                      The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 12                                                                                             The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 13                               The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 14                                                                                                                                                    The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 15                                                                                      The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 16                                                                                                                                                                                        The AUCall calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 17                                                                                                                                                                                    The AUCinf,obs calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 18                                                                                                                                                                                              The  calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 19                                                                                                                                                                                    The AUCint,all calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 20                                                                                                                                                                                   The AUCint,last calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 21                                                                                                                                                                                       The AUClast calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 22                                                                                                                                                                                                             The back-extrapolation percent for intravenous dosing based on AUCall #> 23                                                                                                                                                                                                         The back-extrapolation percent for intravenous dosing based on AUCinf,obs #> 24                                                                                                                                                                                                        The back-extrapolation percent for intravenous dosing based on AUCinf,pred #> 25                                                                                                                                                                                                         The back-extrapolation percent for intravenous dosing based on AUCint,all #> 26                                                                                                                                                                                                        The back-extrapolation percent for intravenous dosing based on AUCint,last #> 27                                                                                                                                                                                                            The back-extrapolation percent for intravenous dosing based on AUClast #> 28                                                                                                                                        The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 29                                                                                                                                                                                                                                                           Dose normalized auclast #> 30                                                                                                                                                                                         Percent of the AUCinf that is extrapolated after Tlast calculated from the observed Clast #> 31                                                                                                                                                                                        Percent of the AUCinf that is extrapolated after Tlast calculated from the predicted Clast #> 32  The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification plus the moment of the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 33                                                                                                                                                                                                                                                           Dose normalized aumcall #> 34                                                                                                                          The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 35                                                                                                                                                                                                                                                       Dose normalized aumcinf.obs #> 36                                                                                                                                                                                                                                                      Dose normalized aumcinf.pred #> 37                                                                                                                                 The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification #> 38                                                                                                                                                                                                                                                          Dose normalized aumclast #> 39                                                                                                                                                                                                                                           Initial concentration after an IV bolus #> 40                                                                                                                                                                                                                                      The average concentration during an interval #> 41                                                                                                                                                                                                                                                               Dose normalized cav #> 42                                                                                                                                                                                                                                              Concentration at the end of infusion #> 43                                                                                                                                                                                                                       Clearance or observed oral clearance calculated with AUCall #> 44                                                                                                                                                                                                                          Clearance or observed oral clearance calculated to Clast #> 45                                                                                                                                                                                                               Clearance or observed oral clearance calculated with observed Clast #> 46                                                                                                                                                                                                              Clearance or observed oral clearance calculated with predicted Clast #> 47                                                                                                                                                                                                                 The last concentration observed above the limit of quantification #> 48                                                                                                                                                                                                                          The concentration at Tlast as predicted by the half-life #> 49                                                                                                                                                                                                                                                        Dose normalized clast.pred #> 50                                                                                                                                                                                                                                      The renal clearance calculated using AUClast #> 51                                                                                                                                                                                                                                   The renal clearance calculated using AUCinf,obs #> 52                                                                                                                                                                                                                                  The renal clearance calculated using AUCinf,pred #> 53                                                                                                                                                                                                                                                    Maximum observed concentration #> 54                                                                                                                                                                                                                                                              Dose normalized cmax #> 55                                                                                                                                                                                                                                                    Minimum observed concentration #> 56                                                                                                                                                                                                                                                              Dose normalized cmin #> 57                                                                                                                                                                                                                                        The trough (end of interval) concentration #> 58                                                                                                                                                                                                                                                           Dose normalized ctrough #> 59                                                                                                                                                                                                                                                             Degree of fluctuation #> 60                                                                                                                                                                                                                                Ending time of the interval (potentially infinity) #> 61                                                                                                                                                                                                                                       Bioavailability or relative bioavailability #> 62                                                                                                                                                                                                                                                 The fraction of the dose excreted #> 63                                                                                                                                                                                                                                                          The (terminal) half-life #> 64                                                                                                                                                                                                                     Elimination rate (as calculated from the intravenous MRTlast) #> 65                                                                                                                                                                                                                      Elimination rate (as calculated from the intravenous MRTobs) #> 66                                                                                                                                                                                                                     Elimination rate (as calculated from the intravenous MRTpred) #> 67                                                                                                                                                                                                                       Elimination rate (as calculated from the MRT using AUClast) #> 68                                                                                                                                                                                                                 Elimination rate (as calculated from the MRT with observed Clast) #> 69                                                                                                                                                                                                                Elimination rate (as calculated from the MRT with predicted Clast) #> 70                                                                                                                                                                                                                                    The elimination rate of the terminal half-life #> 71                                                                                                                                                                                                                        The number of points used for the calculation of half-life #> 72                                                                                                                                                                                                                        The first time point used for the calculation of half-life #> 73                                                                                                                                                                           The mean residence time to the last observed concentration above the LOQ correcting for dosing duration #> 74                                                                                                                                                                                           The mean residence time to infinity using observed Clast correcting for dosing duration #> 75                                                                                                                                                                                          The mean residence time to infinity using predicted Clast correcting for dosing duration #> 76                                                                                                                                                                                                          The mean residence time to the last observed concentration above the LOQ #> 77                                                                                                                                                                                          The mean residence time with multiple dosing and nonlinear kinetics using observed Clast #> 78                                                                                                                                                                                         The mean residence time with multiple dosing and nonlinear kinetics using predicted Clast #> 79                                                                                                                                                                                                                          The mean residence time to infinity using observed Clast #> 80                                                                                                                                                                                                                         The mean residence time to infinity using predicted Clast #> 81                                                                                                                                                                                                                                                   Peak-to-Trough ratio (fraction) #> 82                                                                                                                                                                                                                                        The r^2 value of the half-life calculation #> 83                                                                                                                                                                                                         The ratio of the half-life to the duration used for half-life calculation #> 84                                                                                                                For sparse PK sampling, the area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 85                                                                                                                                                                                                                                                     Starting time of the interval #> 86                                                                                                                                                                                                                                                            Swing relative to Cmin #> 87                                                                                                                                                                                                                 Time of the first concentration above the limit of quantification #> 88                                                                                                                                                                                                              The effective half-life (as determined from the intravenous MRTlast) #> 89                                                                                                                                                                                                               The effective half-life (as determined from the intravenous MRTobs) #> 90                                                                                                                                                                                                              The effective half-life (as determined from the intravenous MRTpred) #> 91                                                                                                                                                                                                                          The effective half-life (as determined from the MRTlast) #> 92                                                                                                                                                                                                                           The effective half-life (as determined from the MRTobs) #> 93                                                                                                                                                                                                                          The effective half-life (as determined from the MRTpred) #> 94                                                                                                                                                                                                                                                  Time above a given concentration #> 95                                                                                                                                                                                                                                                                          Lag time #> 96                                                                                                                                                                                                         Time of the last concentration observed above the limit of quantification #> 97                                                                                                                                                                                                           Apparent observed volume of distribution calculated with observed Clast #> 98                                                                                                                                                                                                          Apparent observed volume of distribution calculated with predicted Clast #> 99                                                                                                                                                                                       The steady-state volume of distribution with intravenous infusion calculating through Tlast #> 100                                                                                                                                                                                           The steady-state volume of distribution with intravenous infusion using observed Clast #> 101                                                                                                                                                                                          The steady-state volume of distribution with intravenous infusion using predicted Clast #> 102                                                                                                                                                                                                                The steady-state volume of distribution calculating through Tlast #> 103                                                                                                                                                                                    The steady-state volume of distribution for nonlinear multiple-dose data using observed Clast #> 104                                                                                                                                                                                   The steady-state volume of distribution for nonlinear multiple-dose data using predicted Clast #> 105                                                                                                                                                                                                                     The steady-state volume of distribution using observed Clast #> 106                                                                                                                                                                                                                    The steady-state volume of distribution using predicted Clast #> 107                                                                                                                                                                                                                         The terminal volume of distribution using observed Clast #> 108                                                                                                                                                                                                                        The terminal volume of distribution using predicted Clast #>"},{"path":"/reference/NCA_fetch_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch ruminate State — NCA_fetch_state","title":"Fetch ruminate State — NCA_fetch_state","text":"Merges default app options changes made UI","code":""},{"path":"/reference/NCA_fetch_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch ruminate State — NCA_fetch_state","text":"","code":"NCA_fetch_state(   id,   input,   session,   FM_yaml_file,   MOD_yaml_file,   id_ASM,   id_UD,   id_DW,   react_state )"},{"path":"/reference/NCA_fetch_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch ruminate State — NCA_fetch_state","text":"id Shiny module ID input Shiny input variable session Shiny session variable FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id_ASM ID string app state management module used save load app states id_UD ID string upload data module used save load app states id_DW ID string data wrangling module used save load app states react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/NCA_fetch_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch ruminate State — NCA_fetch_state","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. NCA: ana_cntr:       Analysis counter. anas:                    List analyses: analysis following  structure: ana_dsview:       Dataset view/ID (name DSV) selected data source analysis. ana_scenario:     Analysis scenario selected UI checksum:         checksum analysis (used detect changes analysis). code:             Code generate analysis start finish error messages code generation/analysis failed. code_components:  List containing different components code col_conc:         Column ana_dsview containing concentration data. col_dose:         Column ana_dsview containing dose amount. col_dur:          Column ana_dsview containing infusion duration N/unused. col_group:        Columns ana_dsview containing grouping variables. col_id:           Column ana_dsview containing subject IDs. col_ntime:        Column ana_dsview containing nominal time values col_route:        Column ana_dsview containing dosing route. col_time:         Column ana_dsview containing time values. id:               Character id (ana_idx). idx:              Numeric id (1). include_units:    Boolean variable indicating units included analysis. interval_range:   Vector first element representing beginning interval second element containing end interval. intervals:        List intervals include. isgood:           Current status analysis. key:              Analysis key acts title/caption (user editable) msgs:             Messages generated checking configuration analysis options. nca_config:       List NCA configuration options analysis. nca_object_name:  Prefix NCA objects associated analyis. nca_parameters:   NCA parameters selected calculation UI. notes:            Analysis notes  (user editable) objs:             List names values objects created generated code. sampling:         Sampling method either \"sparse\" \"serial\" units_amt:        Amount units. units_conc:       Concentration units. units_dose:       Dosing units. units_time:       Time units. current_ana:           Currently selected analysis (list name element anas). DSV:                   Available data source views (see FM_fetch_ds) checksum:              MD5 sum module (checksum analysis checksums). nca_config:            List PKNCA configuration options analysis. nca_parameters:        List two elements choices:          List consisting \"Common Parameters\" \"\" (used grouping UI). list text parameter names value PKNCA parameter name. summary:          Summary table following columns: parameter:      PKNCA Paramter name. text:           Name used text output. md:             Name used markdown output. latex:          Name used latex output. description:    Verbose textual description parameter. ui:                    Current value form elements UI. ui_ana_map:            Map UI element names analysis object get NCA_fetch_current_ana ui_ids:                Vector UI elements module. ui_hold:               List hold elements disable updates full ui referesh complete. MOD_TYPE: Character data containing type module \"NCA\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/NCA_fetch_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch ruminate State — NCA_fetch_state","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:e30844b54d209fd166d1a06622813fc3                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_find_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Determines Default Column Name — NCA_find_col","title":"Determines Default Column Name — NCA_find_col","text":"Based current analysis, value UI, optional list patterns search, column names dataset function tries find default value column analysis (e.g. subject id, dose, concentration, etc). Generally following done: curr_ui non-NULL, non-\"\" value compared dscols. found value returned. patterns considered. patterns YAML file NULL compared sequentially columns names. first match found returned. nothing found first value dscols returned.","code":""},{"path":"/reference/NCA_find_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determines Default Column Name — NCA_find_col","text":"","code":"NCA_find_col(   curr_ana = NULL,   curr_ui = NULL,   patterns = NULL,   dscols,   null_ok = FALSE )"},{"path":"/reference/NCA_find_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determines Default Column Name — NCA_find_col","text":"curr_ana Current value analysis curr_ui Current value UI patterns List regular expression patterns consider. dscols Columns dataset. null_ok Logical value indicating null result (nothing found) OK (default: FALSE)","code":""},{"path":"/reference/NCA_find_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determines Default Column Name — NCA_find_col","text":"Name column found based rules .","code":""},{"path":"/reference/NCA_find_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determines Default Column Name — NCA_find_col","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:e48e1227fb3ea9eb68a408c3016ff336                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_init_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize NCA Module State — NCA_init_state","title":"Initialize NCA Module State — NCA_init_state","text":"Creates list initialized module state","code":""},{"path":"/reference/NCA_init_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize NCA Module State — NCA_init_state","text":"","code":"NCA_init_state(FM_yaml_file, MOD_yaml_file, id, id_UD, id_DW, session)"},{"path":"/reference/NCA_init_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize NCA Module State — NCA_init_state","text":"FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id ID string module. id_UD ID string upload data module used handle uploads name list element react_state data set stored. id_DW ID string data wrangling module process uploaded data session Shiny session variable (app) list (outside app)","code":""},{"path":"/reference/NCA_init_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize NCA Module State — NCA_init_state","text":"list containing empty NCA state","code":""},{"path":"/reference/NCA_load_scenario.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads Pre-Defined Scenario — NCA_load_scenario","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"Loads pre-defined analysis scneario NCA YAML config file.","code":""},{"path":"/reference/NCA_load_scenario.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"","code":"NCA_load_scenario(state, ana_scenario)"},{"path":"/reference/NCA_load_scenario.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"state NCA state NCA_fetch_state() ana_scenario Short name analysis scenario load config file.","code":""},{"path":"/reference/NCA_load_scenario.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"NCA state object scenario loaded relevant notifications set.","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch PKNCA Results Object — NCA_mkactive_ana","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"Fetches PKNCA output specified analysis","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"","code":"NCA_mkactive_ana(state, ana_id)"},{"path":"/reference/NCA_mkactive_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"state NCA state NCA_fetch_state() ana_id Analysis ID make active.","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"State analysis ID made active. JMH add example script ","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:b269fe481810a61478d61c29538bda42                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_new_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize New Analysis — NCA_new_ana","title":"Initialize New Analysis — NCA_new_ana","text":"Creates new NCA analysis NCA module","code":""},{"path":"/reference/NCA_new_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize New Analysis — NCA_new_ana","text":"","code":"NCA_new_ana(state)"},{"path":"/reference/NCA_new_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize New Analysis — NCA_new_ana","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_new_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize New Analysis — NCA_new_ana","text":"NCA state object containing new empty analysis analysis set current active analyisis","code":""},{"path":"/reference/NCA_new_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize New Analysis — NCA_new_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:b972ae6a0a3e49d3e4952575f68ee62c                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_process_current_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Processes Current Analysis to be Run — NCA_process_current_ana","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"Takes current analysis checks different aspects issues make sure good go.","code":""},{"path":"/reference/NCA_process_current_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"","code":"NCA_process_current_ana(state)"},{"path":"/reference/NCA_process_current_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_process_current_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"Current analysis list isgood msgs set","code":""},{"path":"/reference/NCA_process_current_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:90c7e0468c17f9b7317106fa5207b923                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_set_current_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets Current Analysis — NCA_set_current_ana","title":"Sets Current Analysis — NCA_set_current_ana","text":"Takes NCA state analysis list sets figure list value active figure","code":""},{"path":"/reference/NCA_set_current_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets Current Analysis — NCA_set_current_ana","text":"","code":"NCA_set_current_ana(state, ana)"},{"path":"/reference/NCA_set_current_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets Current Analysis — NCA_set_current_ana","text":"state NCA state NCA_fetch_state() ana Analysis list NCA_fetch_current_ana","code":""},{"path":"/reference/NCA_set_current_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets Current Analysis — NCA_set_current_ana","text":"State current analysis updated","code":""},{"path":"/reference/NCA_set_current_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets Current Analysis — NCA_set_current_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:bcad862065667dade418900e45c043b6                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_test_mksession.html","id":null,"dir":"Reference","previous_headings":"","what":"Populate Session Data for Module Testing — NCA_test_mksession","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"Populates supplied session variable testing.","code":""},{"path":"/reference/NCA_test_mksession.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"","code":"NCA_test_mksession(   session,   id = \"NCA\",   id_UD = \"UD\",   id_DW = \"DW\",   id_ASM = \"ASM\",   full_session = TRUE )"},{"path":"/reference/NCA_test_mksession.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"session Shiny session variable (app) list (outside app) id ID string corresponds ID used call modules UI elements id_UD ID string corresponds ID used call UD modules UI elements id_DW ID string corresponds ID used call DW modules UI elements id_ASM ID string corresponds ID used call ASM modules UI elements full_session Boolean indicate full test session created (default TRUE).","code":""},{"path":"/reference/NCA_test_mksession.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"list following elements isgood: Boolean indicating exit status function. session: value Shiny session variable (app) list (outside app) initialization. input: value shiny input end session initialization. state: App state. rsc: react_state components.","code":""},{"path":"/reference/NCA_test_mksession.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"","code":"sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:041729aa6c32af204b5b09837ba504b6"},{"path":"/reference/apply_route_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Route Mapping to Dataset — apply_route_map","title":"Applies Route Mapping to Dataset — apply_route_map","text":"Used convert nonstandard dose route values (.e. \"IV\") standard values (\"intravascular\").","code":""},{"path":"/reference/apply_route_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Route Mapping to Dataset — apply_route_map","text":"","code":"apply_route_map(route_map = list(), route_col = NULL, DS = NULL)"},{"path":"/reference/apply_route_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Route Mapping to Dataset — apply_route_map","text":"route_map List names corresponding route replacement vector regular expressions match. route_col Column name route data. DS Dataframe containing dataset.","code":""},{"path":"/reference/apply_route_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Route Mapping to Dataset — apply_route_map","text":"Dataset route mapping applied.","code":""},{"path":"/reference/apply_route_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies Route Mapping to Dataset — apply_route_map","text":"","code":"if(system.file(package=\"readxl\") !=\"\"){ library(readxl) #loading a dataset data_file =  system.file(package=\"formods\",\"test_data\",\"TEST_DATA.xlsx\") myDS = readxl::read_excel(path=data_file, sheet=\"DATA\")   route_map = list(    intravascular = c(\"^(?i)iv$\"),    extravascular = c(\"^(?i)sc$\", \"^(?i)oral\")  )  utils::head(myDS[[\"ROUTE\"]])  myDS = apply_route_map(route_map = route_map,                        route_col = \"ROUTE\",                        DS        = myDS)  utils::head(myDS[[\"ROUTE\"]]) } #> [1] \"intravascular\" \"intravascular\" \"intravascular\" \"intravascular\" #> [5] \"intravascular\" \"intravascular\""},{"path":"/reference/build_span.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct Table Span From Components — build_span","title":"Construct Table Span From Components — build_span","text":"Takes large table, common rows, header information constructs table subset components using supplied ranges rows columns.","code":""},{"path":"/reference/build_span.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct Table Span From Components — build_span","text":"","code":"build_span(   table_body = NULL,   row_common = NULL,   table_body_head = NULL,   row_common_head = NULL,   header_format = \"text\",   obnd = NULL,   row_sel = NULL,   col_sel = NULL,   table_alignment = \"center\",   inner_border = officer::fp_border(color = \"black\", width = 0.3),   outer_border = officer::fp_border(color = \"black\", width = 2),   set_header_inner_border_v = TRUE,   set_header_inner_border_h = TRUE,   set_header_outer_border = TRUE,   set_body_inner_border_v = TRUE,   set_body_inner_border_h = FALSE,   set_body_outer_border = TRUE,   notes_detect = NULL )"},{"path":"/reference/build_span.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct Table Span From Components — build_span","text":"table_body Data frame body large table. row_common Data frame common rows. table_body_head Data frame matrix headers table body. row_common_head Data frame matrix headers common rows. header_format Format header either \"text\" (default) \"md\" markdown. obnd Optional onbrand object used format markdown. default NULL value use default formatting. row_sel Indices rows build table . col_sel Indices columns build table . table_alignment Character string specifying alignment #'table (body headers). Can \"center\" (default), \"left\", \"right\", \"justify\" inner_border Border object inner border lines defined using officer::fp_border() outer_border Border object outer border lines defined using officer::fp_border() set_header_inner_border_v Boolean value enable disable inner vertical borders headers set_header_inner_border_h Boolean value enable disable inner horizontal borders headers set_header_outer_border Boolean value enable disable outer border headers set_body_inner_border_v Boolean value enable disable inner vertical borders body set_body_inner_border_h Boolean value enable disable inner horizontal borders body set_body_outer_border Boolean value enable disable outer border borders body notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")).","code":""},{"path":"/reference/build_span.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct Table Span From Components — build_span","text":"list following elements df:     Data frame built table. ft:     data frame flextable object. notes:  Note placeholders found table.","code":""},{"path":"/reference/build_span.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct Table Span From Components — build_span","text":"way data frames relate mapped . dimensions different data frames identified (nrow x ncol)","code":"#                            col_sel #                      |<--------------->| # #|--------------------------------------------| --- #|                 |   .                 .    |  ^ #|                 |   .                 .    |  | #| row_common_head |   . table_body_head .    |  | m #|      m x n      |   .      m x c      .    |  | #|                 |   .                 .    |  v #|--------------------------------------------| --- #|                 |   .                 .    |  ^ #|                 |   .                 .    |  | #|    row_common   |   .   table_body    .    |  | #|      r x n      |   .     r x c       .    |  | #|                 |   .                 .    |  | #|.................|..........................|  |     - #|                 |   ./  /  /  /  /  / .    |  |     ^ #|                 |   .  /  /  /  /  /  .    |  | r   | #|                 |   . /  /  /  /  /  /.    |  |     | row_sel #|                 |   ./  /  /  /  /  / .    |  |     | #|                 |   .  /  /  /  /  /  .    |  |     v #|.................|...../../../../../../.... |  |     - #|                 |   .                 .    |  | #|                 |   .                 .    |  v #|--------------------------------------------| --- # #|<--------------->|<------------------------>| #        n                    c"},{"path":"/reference/build_span.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct Table Span From Components — build_span","text":"","code":"library(formods) library(readxl) library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union library(tidyr)  # First we need to create some data.  # This will read in a test dataset: DS = readxl::read_excel(  path  = system.file(package=\"formods\",                       \"test_data\",                       \"TEST_DATA.xlsx\"),  sheet = \"DATA\")  # This will filter the dataset down and modify the formatting DS = dplyr::filter(DS, EVID == 0)                          |>   dplyr::filter(ID <= 30)                                  |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT  = ifelse(.data[[\"CMT\"]] == \"C_ng_ml\",                               \"Test Article\",                               .data[[\"CMT\"]]))              |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT = ifelse(.data[[\"CMT\"]] == \"BM_ng_ml\",                              \"Biomarker\",                              .data[[\"CMT\"]]))               |>   dplyr::rename(Analyte = \"CMT\")                           |>   dplyr::mutate(DV = ifelse(.data[[\"DV\"]] == 0, \"BQL\", .data[[\"DV\"]]))  # This represents the large table we want to split up into smaller tables  wide_df = tidyr::pivot_wider(DS,   values_from = \"DV\",   names_from  = \"ID\")               |>   dplyr::arrange(Analyte, TIME)     |>   dplyr::mutate(TIME = as.character(.data[[\"TIME\"]]))      # The first two columns represent the rows # that are common across the tables: row_common = wide_df[,1:2]  # The remaining columns represent the body of the table: table_body = wide_df[,3:ncol(wide_df)]  # Next we create matrices that contain the header  # information for each component above: row_common_head = matrix(   data  = c(\"Time\", \"Analyte\",             \"(hr)\", \"(ng/ml)\"),   ncol  = 2,   byrow = TRUE)  table_body_head = matrix(   data  = c(rep(\"Subject ID\", times=ncol(table_body)),              names(table_body)),   ncol  = ncol(table_body),   byrow = TRUE)   # This builds all of the tables: span_res = span_table(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       notes_detect    = c(\"BQL\"))  # This will show you the first one: span_res$tables[[\"Table 1\"]]$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`  #> header has 2 row(s)  #> body has 20 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4  # This will build a single table with the rows and  # column ranges specified bs_res =   build_span(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       col_sel         = c(1:10),                       row_sel         = c(1:10),                       notes_detect    = c(\"BQL\"))  # This will show you the result: bs_res$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`  #> header has 2 row(s)  #> body has 10 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6   7    8    9    10 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 643 1050 1130   744 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 644 1051 1134 745.9 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 645 1052 1138 747.8 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 647 1055 1146 751.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4 651 1059 1162 759.2"},{"path":"/reference/dose_records_builder.html","id":null,"dir":"Reference","previous_headings":"","what":"Builds Dose Records Dataframe — dose_records_builder","title":"Builds Dose Records Dataframe — dose_records_builder","text":"Takes information columns dataset constructs dosing records.","code":""},{"path":"/reference/dose_records_builder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Builds Dose Records Dataframe — dose_records_builder","text":"","code":"dose_records_builder(   NCA_DS = NULL,   dose_from = NULL,   col_id = NULL,   col_time = NULL,   col_ntime = NULL,   col_route = NULL,   col_dose = NULL,   col_cycle = NULL,   col_dur = NULL,   col_evid = NULL,   col_analyte = NULL,   col_group = NULL )"},{"path":"/reference/dose_records_builder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Builds Dose Records Dataframe — dose_records_builder","text":"NCA_DS Dataset containing dosing records. dose_from Method dose extraction either \"cols\" \"rows\". col_id Name column subject ID. col_time Name column time since first dose. col_ntime Name column time since last dose (required dose_from=\"cols\"). col_route Name column route information. col_dose Name column last dose given. col_cycle Name column dose cycle (required dose_from=\"cols\"). col_dur Name column dose duration. col_evid Name column event ID (required dose_from=\"rows\"). col_analyte Name column analyte (optional). col_group Names columns grouping information (optionl).","code":""},{"path":"/reference/dose_records_builder.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Builds Dose Records Dataframe — dose_records_builder","text":"list containing following elements isgood:           Return status function. msgs:             Messages passed back user. dose_rec:","code":""},{"path":"/reference/dose_records_builder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Builds Dose Records Dataframe — dose_records_builder","text":"","code":"if(system.file(package=\"readxl\") !=\"\"){ library(dplyr) library(readxl) library(stringr)  # Example data file: data_file =  system.file(package=\"formods\",\"test_data\",\"TEST_DATA.xlsx\")  # Dataset formatted to extract dosing from columns DS_cols = readxl::read_excel(path=data_file, sheet=\"DATA\")        |>  dplyr::filter(EVID == 0)                                |>  dplyr::filter(DOSE %in% c(3))                           |>  dplyr::filter(str_detect(string=Cohort, \"^MD\"))         |>  dplyr::filter(CMT == \"C_ng_ml\")  drb_res = dose_records_builder(  NCA_DS     = DS_cols,  dose_from  = \"cols\",  col_id     = \"ID\",  col_time   = \"TIME_DY\",  col_ntime  = \"NTIME_DY\",  col_route  = \"ROUTE\",  col_cycle  = \"DOSE_NUM\",  col_dose   = \"DOSE\",  col_group  = \"Cohort\")  utils::head(drb_res$dose_rec)  # Dataset formatted to extract dosing from rows (records) DS_rows = readxl::read_excel(path=data_file, sheet=\"DATA\")        |>  dplyr::filter(DOSE %in% c(3))                                   |>  dplyr::filter(str_detect(string=Cohort, \"^MD\"))                 |>  dplyr::filter(CMT %in% c(\"Ac\", \"C_ng_ml\"))  drb_res = dose_records_builder(  NCA_DS     = DS_rows,  dose_from  = \"rows\",  col_id     = \"ID\",  col_time   = \"TIME_DY\",  col_ntime  = \"NTIME_DY\",  col_route  = \"ROUTE\",  col_dose   = \"AMT\",  col_evid   = \"EVID\",  col_group  = \"Cohort\")  utils::head(drb_res$dose_rec) } #> Warning: package ‘dplyr’ was built under R version 4.2.3 #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> Warning: package ‘stringr’ was built under R version 4.2.3 #> # A tibble: 0 × 5 #> # ℹ 5 variables: ID <dbl>, TIME_DY <dbl>, AMT <dbl>, ROUTE <chr>, Cohort <chr>"},{"path":"/reference/fetch_rxinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Information from an rxode2 Object — fetch_rxinfo","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"provide information like parameter names, covriates, etc rxode2 object.","code":""},{"path":"/reference/fetch_rxinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"","code":"fetch_rxinfo(object)"},{"path":"/reference/fetch_rxinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"object rxode2 model object  ID string corresponds ID used call modules UI elements","code":""},{"path":"/reference/fetch_rxinfo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"List following elements. elements: List names simulation elements: covariates: Names covariates system. parameters: Names parameters (subject level) system. iiv: Names iiv parameters system. states: Names states/compartments system.","code":""},{"path":"/reference/fetch_rxinfo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"","code":"library(formods) #> Loading formods library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #> Warning: package ‘rxode2’ was built under R version 4.2.3 #> rxode2 2.1.2 using 1 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> ======================================== #> rxode2 has not detected OpenMP support and will run in single-threaded mode #> This is a Mac. Please read https://mac.r-project.org/openmp/ #> ======================================== #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/fetch_rxtc.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts Timecourse and Merges Covariates — fetch_rxtc","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"Takes output rxSolve() merges missing covariates present params sim","code":""},{"path":"/reference/fetch_rxtc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"","code":"fetch_rxtc(rx_details, sim)"},{"path":"/reference/fetch_rxtc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"rx_details Output fetch_rxinfo() sim output rxSolve()","code":""},{"path":"/reference/fetch_rxtc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"Dataframe simulated time course.","code":""},{"path":"/reference/ft_apply_md.html","id":null,"dir":"Reference","previous_headings":"","what":"Render Markdown in flextable Object — ft_apply_md","title":"Render Markdown in flextable Object — ft_apply_md","text":"Takes flextable object renders markdown specified part.","code":""},{"path":"/reference/ft_apply_md.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Render Markdown in flextable Object — ft_apply_md","text":"","code":"ft_apply_md(ft, obnd = NULL, part = \"body\")"},{"path":"/reference/ft_apply_md.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Render Markdown in flextable Object — ft_apply_md","text":"ft Flextable object. obnd Optional onbrand object used format markdown. default NULL value use default formatting. part Part table can one \"\", \"body\" (default), \"header\", \"footer\"","code":""},{"path":"/reference/ft_apply_md.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Render Markdown in flextable Object — ft_apply_md","text":"flextable markdown applied","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"Takes output PKNCA creates ggplot figures faceted subject id highlighting certain NCA aspects (e.g. points used half-life)","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"","code":"mk_figure_ind_obs(   nca_res,   OBS_LAB = \"Concentration ===CONCUNITS===\",   TIME_LAB = \"Time ===TIMEUNITS===\",   OBS_STRING = \"Observation\",   BLQ_STRING = \"BLQ\",   NA_STRING = \"Missing\",   log_scale = TRUE,   scales = \"fixed\",   nfrows = 4,   nfcols = 3 )"},{"path":"/reference/mk_figure_ind_obs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"nca_res Output PKNCA. OBS_LAB Label observation axis optional ===CONCUNITS=== placeholder units. TIME_LAB Label time axis optional ===TIMEUNITS=== placeholder units. OBS_STRING Label observation data. BLQ_STRING Label BLQ data. NA_STRING Label missing data. log_scale Boolean variable control y-scale (TRUE: Log 10, FALSE: linear). scales String determine scales used faceting. Can either \"fixed\", \"free\", \"free_x\", \"free_y\". nfrows Number facet rows per page. nfcols Number facet cols per page.","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"List containing element figures list figure pages (\"Figure 1\", \"Figure 2\", etc.). also list containing two elements: gg:    ggplot object page. notes: Placeholder future notes, NULL now.","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"","code":"id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a state variable to be define sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:19f16a93834ab4eb3b18d984ccca5b55  state = sess_res$state  # Pulls out the active analysis current_ana = NCA_fetch_current_ana(state)  # This is the raw PKNCA output pknca_res = NCA_fetch_ana_pknca(state, current_ana)  # Building the figure mk_res = mk_figure_ind_obs(nca_res = pknca_res) mk_res$figures$Figure_1$gg"},{"path":"/reference/mk_rx_obj.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes an rxode2 Object — mk_rx_obj","title":"Makes an rxode2 Object — mk_rx_obj","text":"Creates rxode2 object model (either rxode2 function NONMEM file)","code":""},{"path":"/reference/mk_rx_obj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes an rxode2 Object — mk_rx_obj","text":"","code":"mk_rx_obj(type, model)"},{"path":"/reference/mk_rx_obj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes an rxode2 Object — mk_rx_obj","text":"type Type supplied model can \"rxode2\", \"NONMEM\" model List containing relevant information model. depend model types. rxode2: supplied model rxode2 format. fcn_def: Character string containing function definition. fcn_obj: Name funciton object created fcn_def. NONMEM: supplied model NONMEM format (either control model_file: Character string containing NONMEM model file.","code":""},{"path":"/reference/mk_rx_obj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes an rxode2 Object — mk_rx_obj","text":"Results FM_tc() running model. include field isgood boolean variable indicating success failure. See documentation FM_tc() format returned evaluation results failure address . successful capture field contain following: fcn_obj: function name. rx_obj: built rxode2 object.","code":""},{"path":"/reference/mk_rx_obj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Makes an rxode2 Object — mk_rx_obj","text":"","code":"fcn_def = ' my_func = function ()    {        description <- \"One compartment PK model with linear clearance\"        ini({            lka <- 0.45            label(\"Absorption rate (Ka)\")            lcl <- 1            label(\"Clearance (CL)\")            lvc <- 3.45            label(\"Central volume of distribution (V)\")            propSd <- c(0, 0.5)            label(\"Proportional residual error (fraction)\")        })        model({            ka <- exp(lka)            cl <- exp(lcl)            vc <- exp(lvc)            cp <- linCmt()            cp ~ prop(propSd)        })     }' fcn_obj = \"my_func\" model = list(fcn_def = fcn_def,              fcn_obj = fcn_obj)   rx_res = mk_rx_obj(\"rxode2\", model) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # function object rx_res[[\"capture\"]][[\"fcn_obj\"]] #> function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         cp <- linCmt() #>         cp ~ prop(propSd) #>     }) #> } #> <environment: 0x13840ea00>  # rxode2 object rx_res[[\"capture\"]][[\"rx_obj\"]] #>  ── rxode2-based solved PK 1-compartment model with first-order absorption ──────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>    lka    lcl    lvc propSd  #>   0.45   1.00   3.45   0.50  #>  ── Model (Normalized Syntax): ──  #> function() { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         cp <- linCmt() #>         cp ~ prop(propSd) #>     }) #> }"},{"path":"/reference/mk_subjects.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Information from an rxode2 Object — mk_subjects","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"provide information like parameter names, covriates, etc rxode2 object.","code":""},{"path":"/reference/mk_subjects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"","code":"mk_subjects(object, nsub = 10, covs = NULL)"},{"path":"/reference/mk_subjects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"object rxode2 model object  ID string corresponds ID used call modules UI elements. nsub Number subjects generate. covs List describing covariates generated.","code":""},{"path":"/reference/mk_subjects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"List following elements. isgood:     Return status function. msgs:       Error warning messages issues encountered. subjects:   Data frame parameters covariates subjects generated. iCov:       Data frame covariates. params:     Data frame parameters.","code":""},{"path":"/reference/mk_subjects.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"See . underlying simulations run using rxode2, need rxode2 system object. can either simulate subjects load file. Next need define set rules. set conditions actions. evaluation time point conditions evaluated. condition met actions associated condition executed. example, visit (evaluation time point) trough PK certain level (condition) may want increase dosing regimen next dosing cycle (action).","code":""},{"path":"/reference/mk_subjects.html","id":"creating-subjects","dir":"Reference","previous_headings":"","what":"Creating subjects","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/reference/mk_subjects.html","id":"covariates","dir":"Reference","previous_headings":"","what":"Covariates","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. values: field depends type optional sampling . fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/reference/mk_subjects.html","id":"rule-based-simulations","dir":"Reference","previous_headings":"","what":"Rule-based simulations","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/reference/mk_subjects.html","id":"rules","dir":"Reference","previous_headings":"","what":"Rules","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). action: rule trigger can following: type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: action type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: action type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: action type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/reference/mk_subjects.html","id":"rule-evaluation-environment","dir":"Reference","previous_headings":"","what":"Rule-evaluation environment","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/reference/mk_subjects.html","id":"the-following-functions-will-be-available-","dir":"Reference","previous_headings":"","what":"The following functions will be available:","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":[]},{"path":"/reference/mk_subjects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/mk_table_ind_obs.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"Takes output PKNCA creates tabular view individual observation data. can spread several tables (pages) necessary.","code":""},{"path":"/reference/mk_table_ind_obs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"","code":"mk_table_ind_obs(   nca_res,   obnd = NULL,   not_sampled = \"NS\",   blq = \"BLQ\",   digits = 3,   text_format = \"text\",   max_height = 7,   max_width = 6.5,   max_row = NULL,   max_col = 9,   notes_detect = NULL,   rows_by = \"time\" )"},{"path":"/reference/mk_table_ind_obs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"nca_res Output PKNCA. obnd onbrand reporting object. not_sampled Character string use missing data pivoting. blq Character string use indicate data level quantification (value 0 dataset). digits Number significant figures report (set NULL disable rounding) text_format Either \"md\" markdown \"text\" (default) plain text. max_height Maximum height final table inches (value NULL use 100 inches). max_width Maximum width final table inches (value NULL use 100 inches). max_row Maximum number rows page. Spillover hang side page.. max_col Maximum number columns page. Spillover wrapped multiple pages. notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")). rows_by Can either \"time\" \"id\". \"time\", column time separate column subject ID. rows_by set \"id\" column ID column individual time.","code":""},{"path":"/reference/mk_table_ind_obs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"List containing following elements isgood:      Boolean indicating exit status function. one_table:   Dataframe entire table first lines containing header. one_body:    Dataframe entire table (data ). one_header:  Dataframe entire header (row body, data). tables:      Named list tables. list element output msgs:        Vector text messages describing errors found. format build_span.","code":""},{"path":"/reference/mk_table_ind_obs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"Time Concentration (ng/mL) ID (day) 1 2 3 0 BLQ BLQ BLQ 1 690 750 670 2 588 577 576 3 506 508 501 4 438 476 441 7 303 425 320 21 123 274 139 28 95.5 220 104 35 75.8 177 77.8 42 60.6 142 58.6","code":"id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a state variable to be define sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:1f720c87d439d2ce9fb19753e27cd676  state = sess_res$state  # Pulls out the active analysis current_ana = NCA_fetch_current_ana(state)  # This is the raw PKNCA output pknca_res = NCA_fetch_ana_pknca(state, current_ana)  # Building the figure mk_res = mk_table_ind_obs(nca_res = pknca_res) mk_res$tables[[\"Table 1\"]]$ft .cl-4620932a{}.cl-461dc6f4{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-461ee94e{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-461ef182{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef183{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef18c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef18d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef196{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef197{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef198{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1a0{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1a1{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1aa{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1ab{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-461ef1b4{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}TimeConcentration (ng/mL) by ID(day)1230BLQBLQBLQ16907506702588577576350650850144384764417303425320211232741392895.52201043575.817777.84260.614258.6"},{"path":"/reference/mk_table_nca_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Tabular Output from PKNCA Results — mk_table_nca_params","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"Create paginated tables PKNCA use reports Shiny apps.","code":""},{"path":"/reference/mk_table_nca_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"","code":"mk_table_nca_params(   nca_res,   type = \"individual\",   grouping = \"interval\",   not_calc = \"NC\",   obnd = NULL,   nps = NULL,   mult_str = \"*\",   infinity = \"inf\",   digits = NULL,   text_format = \"text\",   notes_detect = NULL,   max_height = 7,   max_width = 6.5,   max_row = NULL,   max_col = NULL )"},{"path":"/reference/mk_table_nca_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"nca_res Output PKNCA. type Type table generate. Can either \"individual\" \"summary\"]. grouping group columns tables. Can either \"interval\" \"parameter\"]. not_calc Text string replace NA values indicated values calculated. obnd onbrand reporting object. nps NCA parameter summary table following columns. parameter:      PKNCA Paramter name. text:           Name used text output. md:             Name used markdown output. latex:          Name used latex output. description:    Verbose textual description parameter. mult_str Text string replace * values units. infinity Text string replace infinity time intervals column headers. digits Number significant figures report (set NULL disable rounding) text_format Either \"md\" markdown \"text\" (default) plain text. notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")). max_height Maximum height final table inches (value NULL use 100 inches). max_width Maximum width final table inches (value NULL use 100 inches). max_row Maximum number rows page. Spillover hang side page.. max_col Maximum number columns page. Spillover wrapped multiple pages.","code":""},{"path":"/reference/mk_table_nca_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"list containing following elements raw_nca:     Raw PKNCA output. isgood:      Boolean indicating exit status function. one_table:   Dataframe entire table first lines containing header. one_body:    Dataframe entire table (data ). one_header:  Dataframe entire header (row body, data). tables:      Named list tables. list element output msgs:        Vector text messages describing errors found. format build_span.","code":""},{"path":"/reference/mk_table_nca_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"ID AUClast Cmax AUCinf,obs  day*ng/mL ng/mL day*ng/mL [0,21] [0,inf] 1 5,900 690 9,590 2 8,240 750 17,000 3 6,140 670 9,520","code":"id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a state variable to be define sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:bbad68490dee0aaebcd3b23b4936bb58  state = sess_res$state  # Pulls out the active analysis current_ana = NCA_fetch_current_ana(state)  # This is the raw PKNCA output pknca_res = NCA_fetch_ana_pknca(state, current_ana)  # Parameter reporting details from the ruminate configuration nps  = state[[\"NCA\"]][[\"nca_parameters\"]][[\"summary\"]]  # Building the figure mk_res = mk_table_nca_params(nca_res = pknca_res, nps=nps, digits=3) mk_res$tables[[\"Table 1\"]]$ft .cl-46faa524{}.cl-46f7ca66{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-46f8e7ac{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-46f8f102{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f10c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f10d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f116{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f117{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f118{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f119{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f120{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f121{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f12a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f12b{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f12c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f134{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-46f8f135{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}IDAUClastCmaxAUCinf,obsday*ng/mLng/mLday*ng/mL[0,21][0,inf]15,9006909,59028,24075017,00036,1406709,520"},{"path":"/reference/nca_builder.html","id":null,"dir":"Reference","previous_headings":"","what":"Builds NCA Code from ui Elements — nca_builder","title":"Builds NCA Code from ui Elements — nca_builder","text":"Takes current analysis state object creates code run analysis","code":""},{"path":"/reference/nca_builder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Builds NCA Code from ui Elements — nca_builder","text":"","code":"nca_builder(state)"},{"path":"/reference/nca_builder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Builds NCA Code from ui Elements — nca_builder","text":"state NCA state NCA_fetch_state() JMH update return list ","code":""},{"path":"/reference/nca_builder.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Builds NCA Code from ui Elements — nca_builder","text":"NCA state NCA current analysis built.","code":""},{"path":"/reference/nca_builder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Builds NCA Code from ui Elements — nca_builder","text":"","code":"# Module IDs id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a module variables to be defined sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:14126a9100b4aee25d0c555032bf1ca4  state = sess_res$state  state = nca_builder(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion"},{"path":"/reference/plot_sr_ev.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots        Timecourse of Rules Simulations — plot_sr_ev","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"","code":"Plots the timecourse of `simulate_rules()` output."},{"path":"/reference/plot_sr_ev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"","code":"plot_sr_ev(   sro = NULL,   fpage = 1,   fcol = \"id\",   error_msgs = NULL,   ylog = TRUE,   ylab_str = \"Amount\",   xlab_str = \"Time\",   post_proc = \"fig  = fig + ggplot2::theme_light()\",   evplot = c(1, 4),   fncol = 4,   fnrow = 2 )"},{"path":"/reference/plot_sr_ev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"sro Output 'simulate_rules()'. fpage facets selected multiple pages generated indcates       page return. fcol Name column facet NULL disable faceting (\"id\"). error_msgs Named list error messages overwrite (NULL ylog Boolean enable log10 scaling y-axis (TRUE ylab_str Label y-axis (\"Output\" xlab_str Label x-axis (\"Output\" post_proc Character object post processing post-processing code figure object named fig internall  (\"fig = fig + theme_light()\") evplot Evids plot can 1 4 fncol Number columns faceted output. fnrow Number rows faceted output.","code":""},{"path":"/reference/plot_sr_ev.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"List followin1g elements: isgood:       Return status function. msgs:         Error warning messages issues encountered. npages:       Total number pages using current configuration. error_msgs:   List error messages used. dsp:          Intermediate dataset generated sro plot ggplot. fig:          Figure generated.","code":""},{"path":"/reference/plot_sr_ev.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"detailed examples see vignette(\"clinical_trial_simulation\", package = \"ruminate\").","code":""},{"path":"/reference/plot_sr_ev.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/plot_sr_tc.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots        Timecourse of Rules Simulations — plot_sr_tc","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"","code":"Plots the timecourse of `simulate_rules()` output."},{"path":"/reference/plot_sr_tc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"","code":"plot_sr_tc(   sro = NULL,   dvcols = NULL,   fpage = 1,   fcol = \"id\",   error_msgs = NULL,   ylog = TRUE,   ylab_str = \"Output\",   xlab_str = \"Time\",   post_proc = \"fig  = fig + ggplot2::theme_light()\",   fncol = 4,   fnrow = 2 )"},{"path":"/reference/plot_sr_tc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"sro Output 'simulate_rules()'. dvcols Character vector dependent variables. fpage facets selected multiple pages generated indcates       page return. fcol Name column facet NULL disable faceting (\"id\"). error_msgs Named list error messages overwrite (NULL ylog Boolean enable log10 scaling y-axis (TRUE ylab_str Label y-axis (\"Output\" xlab_str Label x-axis (\"Output\" post_proc Character object post processing post-processing code figure object named fig internall  (\"fig = fig + theme_light()\") fncol Number columns faceted output. fnrow Number rows faceted output.","code":""},{"path":"/reference/plot_sr_tc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"List followin1g elements: isgood:       Return status function. msgs:         Error warning messages issues encountered. npages:       Total number pages using current configuration. error_msgs:   List error messages used. dsp:          Intermediate dataset generated sro plot ggplot. fig:          Figure generated.","code":""},{"path":"/reference/plot_sr_tc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"detailed examples see vignette(\"clinical_trial_simulation\", package = \"ruminate\").","code":""},{"path":"/reference/plot_sr_tc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/ruminate.html","id":null,"dir":"Reference","previous_headings":"","what":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"done creating Shiny interface different tools data transformation (dplyr tidyr), plotting (ggplot2), noncompartmental analysis (PKNCA). results can reported Excel, Word PowerPoint. state app can saved loaded later date. saved, script generated reproduce different actions Shiny interface. Runs pharmacometrics ruminate app.","code":""},{"path":"/reference/ruminate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"","code":"ruminate(   host = \"127.0.0.1\",   port = 3838,   server_opts = list(shiny.maxRequestSize = 30 * 1024^2),   mksession = FALSE )"},{"path":"/reference/ruminate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"host Hostname server (\"127.0.0.1\") port Port number app (3838) server_opts List options (names) vlues (value) e.g. list(shiny.maxRequestSize = 30 * 1024^2). mksession Boolean value, TRUE load test session data app testing","code":""},{"path":"/reference/ruminate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"Nothing returned, function just runs built-ruminate app.","code":""},{"path":[]},{"path":"/reference/ruminate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"Maintainer: John Harrold john.m.harrold@gmail.com (ORCID)","code":""},{"path":"/reference/ruminate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"","code":"if (interactive()) { ruminate() }"},{"path":"/reference/ruminate_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks ruminate Dependencies — ruminate_check","title":"Checks ruminate Dependencies — ruminate_check","text":"Looks suggested dependencies checks make sure installed.","code":""},{"path":"/reference/ruminate_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks ruminate Dependencies — ruminate_check","text":"","code":"ruminate_check(verbose = TRUE)"},{"path":"/reference/ruminate_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks ruminate Dependencies — ruminate_check","text":"verbose Logical indicating messages displayed","code":""},{"path":"/reference/ruminate_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks ruminate Dependencies — ruminate_check","text":"List following elements: all_found:    Boolean indicating packages found found_pkgs:   Character vector found packages missing_pkgs: Character vector missing packages","code":""},{"path":"/reference/ruminate_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks ruminate Dependencies — ruminate_check","text":"","code":"fcres =ruminate_check() #>  #> ── Checking ruminate for suggested packages ──────────────────────────────────── #> ✔ found clipr #> ✔ found gridExtra #> ✔ found knitr #> ✔ found nlmixr2lib #> ✔ found nonmem2rx #> ✔ found prompter #> ✔ found rmarkdown #> ✔ found readxl #> ✔ found rxode2et #> ✔ found shinydashboard #> ✔ found testthat #> ✔ found ubiquity"},{"path":"/reference/run_nca_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs NCA for the Current Analysis — run_nca_components","title":"Runs NCA for the Current Analysis — run_nca_components","text":"Takes current state runs current analysis state.","code":""},{"path":"/reference/run_nca_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs NCA for the Current Analysis — run_nca_components","text":"","code":"run_nca_components(   state,   components = c(\"nca\", \"fg_ind_obs\", \"tb_ind_obs\", \"tb_ind_params\") )"},{"path":"/reference/run_nca_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs NCA for the Current Analysis — run_nca_components","text":"state NCA state NCA_fetch_state() components List components run. default run following. just need regenerate figure based current nca results can just specify component. valid components: nca:               Run NCA analysis fg_ind_obs:        Build figure(s) indiviudal observations. tb_ind_obs:        Build table(s) indiviudal observations. tb_ind_params:     Build table(s) indiviudal parameters.","code":""},{"path":"/reference/run_nca_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs NCA for the Current Analysis — run_nca_components","text":"List following components: isgood:    Return status function. msgs:      Error messages issues encountered. nca_res:   PKNCA results run successful.","code":""},{"path":"/reference/run_nca_components.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runs NCA for the Current Analysis — run_nca_components","text":"","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:1aa0507d8b25ad9c39353070c684066a state = sess_res$state  state = run_nca_components(state, components=\"tb_ind_params\") #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:1aa0507d8b25ad9c39353070c684066a"},{"path":"/reference/simulate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Rule-Based simulates — simulate_rules","title":"Rule-Based simulates — simulate_rules","text":"Simulate rxode2 model based rules evaluated specified time-points. example want titrate dosing based individual plasma levels create rule changes dosing specified time points based last observation user.","code":""},{"path":"/reference/simulate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rule-Based simulates — simulate_rules","text":"","code":"simulate_rules(   object,   subjects,   eval_times,   output_times,   rules,   rx_options = list(),   preamble = \"\",   smooth_sampling = TRUE )"},{"path":"/reference/simulate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rule-Based simulates — simulate_rules","text":"object rxode2 model object  ID string corresponds ID used call modules UI elements subjects Dataframe subject level information. eval_times Vector simulation times evaulate rules (units time units system). output_times Specific output times include. times included well ensure proper evaluation rules. rules List rules, see description format. rx_options List options pass rxSolve(). preamble Character string user-defined code execute rule-evaluation environment (e.g. can put user-defined functions ). smooth_sampling Boolean TRUE insert sampling just dosing make sampling smooth.","code":""},{"path":"/reference/simulate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rule-Based simulates — simulate_rules","text":"List following elements: isgood:     Return status function. msgs:       Error warning messages issues encountered. simall:     Simulation results. ev_history: event table entire simulation history. eval_times: Evaluation time points","code":""},{"path":"/reference/simulate_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rule-Based simulates — simulate_rules","text":"detailed examples see vignette(\"clinical_trial_simulation\", package = \"ruminate\") underlying simulations run using rxode2, need rxode2 system object. can either simulate subjects load file. Next need define set rules. set conditions actions. evaluation time point conditions evaluated. condition met actions associated condition executed. example, visit (evaluation time point) trough PK certain level (condition) may want increase dosing regimen next dosing cycle (action).","code":""},{"path":"/reference/simulate_rules.html","id":"creating-subjects","dir":"Reference","previous_headings":"","what":"Creating subjects","title":"Rule-Based simulates — simulate_rules","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/reference/simulate_rules.html","id":"covariates","dir":"Reference","previous_headings":"","what":"Covariates","title":"Rule-Based simulates — simulate_rules","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. values: field depends type optional sampling . fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/reference/simulate_rules.html","id":"rule-based-simulations","dir":"Reference","previous_headings":"","what":"Rule-based simulations","title":"Rule-Based simulates — simulate_rules","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/reference/simulate_rules.html","id":"rules","dir":"Reference","previous_headings":"","what":"Rules","title":"Rule-Based simulates — simulate_rules","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). action: rule trigger can following: type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: action type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: action type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: action type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/reference/simulate_rules.html","id":"rule-evaluation-environment","dir":"Reference","previous_headings":"","what":"Rule-evaluation environment","title":"Rule-Based simulates — simulate_rules","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/reference/simulate_rules.html","id":"the-following-functions-will-be-available-","dir":"Reference","previous_headings":"","what":"The following functions will be available:","title":"Rule-Based simulates — simulate_rules","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":"/reference/simulate_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rule-Based simulates — simulate_rules","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/span_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Spread Large Table Over Smaller Tables — span_table","title":"Spread Large Table Over Smaller Tables — span_table","text":"Takes large table spreads smaller tables paginate . preserve common row  information left separate columns according maximum specifications. final tables widths less equal max_col max_width, heights less equal max_row max_height.","code":""},{"path":"/reference/span_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spread Large Table Over Smaller Tables — span_table","text":"","code":"span_table(   table_body = NULL,   row_common = NULL,   table_body_head = NULL,   row_common_head = NULL,   header_format = \"text\",   obnd = NULL,   max_row = 20,   max_col = 10,   max_height = 7,   max_width = 6.5,   table_alignment = \"center\",   inner_border = officer::fp_border(color = \"black\", width = 0.3),   outer_border = officer::fp_border(color = \"black\", width = 2),   set_header_inner_border_v = TRUE,   set_header_inner_border_h = TRUE,   set_header_outer_border = TRUE,   set_body_inner_border_v = TRUE,   set_body_inner_border_h = FALSE,   set_body_outer_border = TRUE,   notes_detect = NULL )"},{"path":"/reference/span_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spread Large Table Over Smaller Tables — span_table","text":"table_body Data frame body large table. row_common Data frame common rows. table_body_head Data frame matrix headers table body. row_common_head Data frame matrix headers common rows. header_format Format header either \"text\" (default) \"md\" markdown. obnd Optional onbrand object used format markdown. default NULL value use default formatting. max_row Maximum number rows output tables (value NULL set max_row number rows table). max_col Maximum number columns output tables (value NULL set max_col number columns table). max_height Maximum height final table inches (value NULL use 100 inches). max_width Maximum width final table inches (value NULL use 100 inches). table_alignment Character string specifying alignment #'table (body headers). Can \"center\" (default), \"left\", \"right\", \"justify\" inner_border Border object inner border lines defined using officer::fp_border() outer_border Border object outer border lines defined using officer::fp_border() set_header_inner_border_v Boolean value enable disable inner vertical borders headers set_header_inner_border_h Boolean value enable disable inner horizontal borders headers set_header_outer_border Boolean value enable disable outer border headers set_body_inner_border_v Boolean value enable disable inner vertical borders body set_body_inner_border_h Boolean value enable disable inner horizontal borders body set_body_outer_border Boolean value enable disable outer border borders body notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")).","code":""},{"path":"/reference/span_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spread Large Table Over Smaller Tables — span_table","text":"list following elements isgood:    Boolean indicating exit status function. one_body:  Full table headers. one_table: Full table headers. msgs:      Vector text messages describing errors found. tables:    Named list tables. list element output. format build_span().","code":""},{"path":"/reference/span_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spread Large Table Over Smaller Tables — span_table","text":"way data frames relate mapped . dimensions different data frames identified (nrow x ncol)","code":"#|-------------------------------------| --- #|                 |                   |  ^ #|                 |                   |  | #| row_common_head |  table_body_head  |  | m #|      m x n      |       m x c       |  | #|                 |                   |  v #|-------------------------------------| --- #|                 |                   |  ^ #|                 |                   |  | #|    row_common   |    table_body     |  | r #|      r x n      |      r x c        |  | #|                 |                   |  | #|                 |                   |  v #|-------------------------------------| --- # #|<--------------->|<----------------->| #        n                   c"},{"path":[]},{"path":"/reference/span_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spread Large Table Over Smaller Tables — span_table","text":"","code":"library(formods) library(readxl) library(dplyr) library(tidyr)  # First we need to create some data.  # This will read in a test dataset: DS = readxl::read_excel(  path  = system.file(package=\"formods\",                       \"test_data\",                       \"TEST_DATA.xlsx\"),  sheet = \"DATA\")  # This will filter the dataset down and modify the formatting DS = dplyr::filter(DS, EVID == 0)                          |>   dplyr::filter(ID <= 30)                                  |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT  = ifelse(.data[[\"CMT\"]] == \"C_ng_ml\",                               \"Test Article\",                               .data[[\"CMT\"]]))              |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT = ifelse(.data[[\"CMT\"]] == \"BM_ng_ml\",                              \"Biomarker\",                              .data[[\"CMT\"]]))               |>   dplyr::rename(Analyte = \"CMT\")                           |>   dplyr::mutate(DV = ifelse(.data[[\"DV\"]] == 0, \"BQL\", .data[[\"DV\"]]))  # This represents the large table we want to split up into smaller tables  wide_df = tidyr::pivot_wider(DS,   values_from = \"DV\",   names_from  = \"ID\")               |>   dplyr::arrange(Analyte, TIME)     |>   dplyr::mutate(TIME = as.character(.data[[\"TIME\"]]))      # The first two columns represent the rows # that are common across the tables: row_common = wide_df[,1:2]  # The remaining columns represent the body of the table: table_body = wide_df[,3:ncol(wide_df)]  # Next we create matrices that contain the header  # information for each component above: row_common_head = matrix(   data  = c(\"Time\", \"Analyte\",             \"(hr)\", \"(ng/ml)\"),   ncol  = 2,   byrow = TRUE)  table_body_head = matrix(   data  = c(rep(\"Subject ID\", times=ncol(table_body)),              names(table_body)),   ncol  = ncol(table_body),   byrow = TRUE)   # This builds all of the tables: span_res = span_table(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       notes_detect    = c(\"BQL\"))  # This will show you the first one: span_res$tables[[\"Table 1\"]]$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`  #> header has 2 row(s)  #> body has 20 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4  # This will build a single table with the rows and  # column ranges specified bs_res =   build_span(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       col_sel         = c(1:10),                       row_sel         = c(1:10),                       notes_detect    = c(\"BQL\"))  # This will show you the result: bs_res$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`  #> header has 2 row(s)  #> body has 10 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6   7    8    9    10 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 643 1050 1130   744 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 644 1051 1134 745.9 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 645 1052 1138 747.8 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 647 1055 1146 751.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4 651 1059 1162 759.2"},{"path":"/news/index.html","id":"ruminate-022-development-version","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.2 (development version)","title":"ruminate 0.2.2 (development version)","text":"CRAN release: 2024-03-14 Added CTS (Clinical trial simulator) module (development)","code":""},{"path":"/news/index.html","id":"ruminate-022","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.2","title":"ruminate 0.2.2","text":"CRAN release: 2024-03-14 Fixed error default ruminate app causing crash Separated ruminate.R sample app two separate (default one one developent.)","code":""},{"path":"/news/index.html","id":"ruminate-021","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.1","title":"ruminate 0.2.1","text":"CRAN release: 2023-12-11 Fixed use suggests conditional.","code":""},{"path":"/news/index.html","id":"ruminate-020","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.0","title":"ruminate 0.2.0","text":"CRAN release: 2023-12-02 Support rxode2 NONMEM models Fixed bug NCA module column details didn’t update properly switching analyses. Fixed bug detecting dosing rows left dosing records data frame subsequent NCA. Fixed bug parameter(s) added interval resulting [[numeric]] reported values. Now time interval added subsequent additions update parameters interval. Fixed stand-alone code generation copy clipboard.","code":""},{"path":"/news/index.html","id":"ruminate-011","dir":"Changelog","previous_headings":"","what":"ruminate 0.1.1","title":"ruminate 0.1.1","text":"CRAN release: 2023-08-29 Initial release","code":""}]
+[{"path":"/articles/clinical_trial_simulation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Clinical Trial Simulation","text":"Normally want simulate patients response define regimen just simulate forward specified point time. works well compounds relatively safe, PK consistent time, drug concentrations required achieve efficacy consistent. fact restrictions apply many different compounds. However, many scenarios necessary adjust dosing based periodic clinical visits: compounds narrow therapeutic index, disease progression alters drug PK requires increases dose amount, etc. possible manually current simulation tools provided, section outlines generalized framework make easier users.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Clinical Trial Simulation","text":"underlying simulations run using rxode2, need rxode2 system object. can either simulate subjects load file. Next need define set rules. set conditions actions. evaluation time point conditions evaluated. condition met actions associated condition executed. example, visit (evaluation time point) trough PK certain level (condition) may want increase dosing regimen next dosing cycle (action).","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"creating-subjects","dir":"Articles","previous_headings":"Overview","what":"Creating subjects","title":"Clinical Trial Simulation","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"covariates","dir":"Articles","previous_headings":"Overview > Creating subjects","what":"Covariates","title":"Clinical Trial Simulation","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/articles/clinical_trial_simulation.html","id":"rule-based-simulations","dir":"Articles","previous_headings":"Overview","what":"Rule-based simulations","title":"Clinical Trial Simulation","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"rules","dir":"Articles","previous_headings":"Overview > Rule-based simulations","what":"Rules","title":"Clinical Trial Simulation","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"rule-evaluation-environment","dir":"Articles","previous_headings":"Overview > Rule-based simulations","what":"Rule-evaluation environment","title":"Clinical Trial Simulation","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/articles/clinical_trial_simulation.html","id":"the-following-functions-will-be-available","dir":"Articles","previous_headings":"Overview > Rule-based simulations > Rule-evaluation environment","what":"The following functions will be available:","title":"Clinical Trial Simulation","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":"/articles/clinical_trial_simulation.html","id":"example-fixed-dosing","dir":"Articles","previous_headings":"","what":"Example: Fixed dosing","title":"Clinical Trial Simulation","text":"explore framework simple system describing antibody PK driving change biomarker indirect response used. timescale model days system shown bottom want look . can also find {rumiante} package :","code":"system.file(package=\"ruminate\", \"test_apps\", \"test_rxode2_system.R\")"},{"path":"/articles/clinical_trial_simulation.html","id":"creating-the-rxode2-object","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Creating the rxode2 object","title":"Clinical Trial Simulation","text":"source example file create function my_model. can convert rxode2 object:","code":"source(system.file(package=\"ruminate\", \"test_apps\", \"test_rxode2_system.R\")) object = rxode2(my_model)"},{"path":"/articles/clinical_trial_simulation.html","id":"defining-subjects","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Defining subjects","title":"Clinical Trial Simulation","text":"first thing want create subjects. can using interindividual variability system. systems covariates need define . example three covariates needed. list create sampling SEX_ID randomly discrete distribution, fixing SUBTYPE_ID 0, sampling WT log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) ) set.seed(8675309) rxode2::rxSetSeed(8675309) subs = mk_subjects(object = object,                    nsub   = 6,                    covs   = covs) ##   id TV_F1 TV_ka     TV_CL    TV_Vc    TV_Vp       TV_Q TV_MW TV_BM_IC ## 1  1 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 2  2 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 3  3 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 4  4 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 5  5 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ## 6  6 0.744 0.282 -1.609438 1.283708 1.011601 -0.2916901   140     1000 ##   TV_kdeg_BM TV_Emax TV_EC50       ETAka       ETACL       ETAVc       ETAVp ## 1        0.1     100     300  0.30065410  0.02638762  0.22298038 -0.03439826 ## 2        0.1     100     300  0.08502272  0.09481141 -0.26303939  0.24911727 ## 3        0.1     100     300 -1.12982057  0.32226445 -0.16893668  0.53718003 ## 4        0.1     100     300  0.41441629 -0.12367437 -0.31273632 -0.07090379 ## 5        0.1     100     300 -0.48897344  0.02604895 -0.03895797 -0.17078974 ## 6        0.1     100     300  1.11064814 -0.65061266 -0.33562320 -0.35589967 ##         ETAQ    ETABM_IC     ETAEmax SEX_ID SUBTYPE_ID       WT ## 1  0.6900363 -0.04931769  0.39039383      0          0 51.00773 ## 2  1.4615575  0.32202532  0.75203484      0          0 81.35479 ## 3 -0.4840149 -0.29372697 -0.04508853      1          0 70.17848 ## 4 -2.7754122  0.25222657 -0.05102237      1          0 73.50821 ## 5  0.2097416 -0.51675088  0.24546028      0          0 68.13168 ## 6 -0.8491960 -0.20730296 -0.06938995      0          0 72.56017"},{"path":"/articles/clinical_trial_simulation.html","id":"creating-rules","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Creating rules","title":"Clinical Trial Simulation","text":"rules named list. list names arbitrary used track going . list two required elements. condition action. condition character string evaluated results either TRUE FALSE. action list type list elements based type. Shown \"dose\" type action: can optionally add true_value false_value stored simulated output indicate evaluation status condition. setting dose type need also provide elements values, times, durations. character objects can evaluated provide vectors. ’s important note following providing dose type action: lengths equal. Times relative evaluation time point.","code":"rules = list(   low_dose = list(     condition = \"TRUE\",     true_value  = \"3mg\",     false_value = \"0\",     action    = list(       type  = \"dose\",       state     = \"Ac\",        values    = \"c(3,  3,  3,  3)*1e6/MW\",       times     = \"c(0, 14, 28, 42)\",       durations = \"c(0,  0,  0,  0)\")     )  )"},{"path":"/articles/clinical_trial_simulation.html","id":"evaluation-times","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Evaluation times","title":"Clinical Trial Simulation","text":"subjects rules defined need define timepoints rules evaluated. case considering 7 cycles (0 6) 8 weeks (2*28) .","code":"eval_times =  (0:6)*28*2"},{"path":"/articles/clinical_trial_simulation.html","id":"running-simulations","dir":"Articles","previous_headings":"Example: Fixed dosing","what":"Running simulations","title":"Clinical Trial Simulation","text":"define output times want simulation. Note last evlation point day 336 (6*2*8), follow timecourse one cycle day 392 (7*2*8). want pass options rxode2 can creating named list option name value paired together: Lastly can passed simulate_rules():","code":"output_times = seq(0, 7*28*2, 10) rx_options = list(    covsInterpolation = \"locf\",    addCov=TRUE ) simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":"/articles/clinical_trial_simulation.html","id":"example-resetting-states","dir":"Articles","previous_headings":"","what":"Example Resetting states","title":"Clinical Trial Simulation","text":"action type \"set state\" can used arbitrarily set value state system. setting central compartment value 10 time zero. time 56 increase value peripheral compartment Cp factor 5.","code":"rules = list(   reset_Ac    = list(    condition = \"time == 0\",    true_flag  = \"Ac set\",    false_flag = \"\",    action    = list(      type     = \"set state\",      state    = \"Ac\",      value    = \"10\")    ),  reset_Cp    = list(    condition = \"time == 56\",    true_flag  = \"Cp set\",    false_flag = \"\",    action    = list(      type     = \"set state\",      state    = \"Cp\",      value    = \"Cp*5\")   ) )  simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":"/articles/clinical_trial_simulation.html","id":"example-manual-code-evaluation","dir":"Articles","previous_headings":"","what":"Example: Manual code evaluation","title":"Clinical Trial Simulation","text":"find need something complicated can include functions preamble use functions methods . Alternatively can create code use \"manual\" rule type modify event table using objects rule evaluation environment . code simply setting state current subject id value.","code":"code=\" SI_interval_ev = etRbind(SI_interval_ev, et(cmt  = 'Ac',    id   = id,    amt  = 10,    evid = 4,    time = time))\"  rules = list(   manual_example = list(     condition = \"time == 56\",     true_flag  = \"manual\",     false_flag = \"\",     action    = list(       type  = \"manual\",       code      = code     )   ) )  simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":"/articles/clinical_trial_simulation.html","id":"example-titrated-dosing-of-a-biomarker","dir":"Articles","previous_headings":"","what":"Example: Titrated dosing of a biomarker","title":"Clinical Trial Simulation","text":"examples demonstrate mechanics perform rule-based simualtions. example provide concrete demonstration use elements together. want titrate dosing biomarker within range. range want maintain dose. first rule active time 0 initialize dosing 0.1 mg. remaining rules active first dose time 0 biomarker range (ss_dose_increase ), range (ss_dose_decrease), range (ss_dose_keep). use simulation internal function SI_fpd fetch previous dose select new dose fraction previous. need can create functions use action fields. just need create character string function definitions pass preamble input simulate_rules().","code":"rules = list(  first_cycle = list(    condition = \"time == 0\",    true_flag  = \"first cycle\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c(0.1,  0.1,  0.1,  0.1)*1e6/MW\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) ,  ss_dose_keep         = list(    condition = \"((BM <=  7e4) & (BM >=5e4)) & (time > 0)\",    true_flag  = \"keep last\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c( 1.0,  1.00,  1.00,  1.00)*SI_fpd(id=id, state='Ac')\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) ,  ss_dose_decrease     = list(    condition = \"(BM >  7e4) & (time > 0)\",    true_flag  = \"titrate down\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c( .90,   .90,   .90,   .90)*SI_fpd(id=id, state='Ac')\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) ,  ss_dose_increase = list(    condition = \"(BM <  5e4) & (time > 0)\",    true_flag  = \"titrate up\",    false_flag = \"\",    action    = list(      type  = \"dose\",      state     = \"Ac\",       values    = \"c(1.30,  1.30,  1.30,  1.30)*SI_fpd(id=id, state='Ac')\",      times     = \"c(0, 14, 28, 42)\",      durations = \"c(0,  0,  0,  0)\")    ) )  simres =  simulate_rules(object        = object,                subjects      = subs[[\"subjects\"]],                eval_times    = eval_times,                output_times  = output_times,                 rules         = rules,                rx_options    = rx_options)"},{"path":[]},{"path":[]},{"path":[]},{"path":"/articles/clinical_trial_simulation.html","id":"antibody-pk-and-biomarker-system","dir":"Articles","previous_headings":"","what":"Antibody-PK and biomarker system","title":"Clinical Trial Simulation","text":"","code":"# Dosing into Ac and At is in nmoles. So to dose 3 mg you would use 3e6/MW my_model <- function() { ini({    # Typical Value of System Parameters     TV_F1           = fixed(c(.Machine$double.eps, 0.744, .Machine$double.xmax))    TV_ka           =       c(.Machine$double.eps, 0.282, .Machine$double.xmax)    TV_CL           =   log(c(.Machine$double.eps, 0.200, .Machine$double.xmax))    TV_Vc           =   log(c(.Machine$double.eps, 3.61, .Machine$double.xmax))    TV_Vp           =   log(c(.Machine$double.eps, 2.75, .Machine$double.xmax))    TV_Q            =   log(c(.Machine$double.eps, 0.747, .Machine$double.xmax))    TV_MW           = fixed(c(.Machine$double.eps, 140, .Machine$double.xmax))    TV_BM_IC        =       c(.Machine$double.eps, 1000, .Machine$double.xmax)    TV_kdeg_BM      = fixed(c(.Machine$double.eps, 0.1, .Machine$double.xmax))    TV_Emax         =       c(.Machine$double.eps, 100.0, .Machine$double.xmax)    TV_EC50         = fixed(c(.Machine$double.eps, 300, .Machine$double.xmax))     # Between-subject variability:      ETAka +     ETACL +     ETAVc +     ETAVp +     ETAQ +      ETABM_IC +  ETAEmax ~    c(0.416,                                                                               0,          0.09875,                                                                  0,          0.0786,     0.116,                                                       0,          0.0619,     0.0377,     0.0789,                                          0,          0,          0,          0,          0.699,                               0,          0,          0,          0,          0,          0.05,                    0,          0,          0,          0,          0,          0,          0.10)       # Error model parameters    prop_err_PK  =  c(.Machine$double.eps, 0.1, .Machine$double.xmax)    add_err_PK   =  c(.Machine$double.eps, 0.1, .Machine$double.xmax)    add_err_BM   =  c(.Machine$double.eps, 0.1, .Machine$double.xmax)  }) model({     # System Parameters     F1          = TV_F1    ka          = TV_ka*exp(ETAka)    CL          = exp(TV_CL)*exp(ETACL)    Vc          = exp(TV_Vc)*exp(ETAVc)    # AMTIFY:Cc    Cc          = Ac/(Vc)    Vp          = exp(TV_Vp)*exp(ETAVp)    Q           = exp(TV_Q)*exp(ETAQ)    MW          = TV_MW    BM_IC       = TV_BM_IC*exp(ETABM_IC)    kdeg_BM     = TV_kdeg_BM    Emax        = TV_Emax*exp(ETAEmax)    EC50        = TV_EC50     Dinf        = 0.0     # Static Secondary Parameters     WTTV        = 70     CL_IND      = CL*(1.0+SEX_ID*.1)*(1.0+SUBTYPE_ID*.1)     kel         = CL_IND/Vc*((WT/WTTV))^(-0.35)     kcp         = Q/Vc*((WT/WTTV))^(-0.35)     kpc         = Q/Vp*((WT/WTTV))^(-0.35)     ksyn_BM     = kdeg_BM*BM_IC      # Dynamic Secondary Parameters     STIM        = 1.0+ Emax*Cc/(EC50+Cc)      # Defining ODEs    BM(0)       = BM_IC    d/dt(At)    = (-ka*At)    d/dt(Ac)    = ((ka*At*F1/Vc  -kel*Cc - kcp*Cc  + kpc*Cp*Vp/Vc + Dinf/Vc))*Vc    d/dt(Cp)    = (kcp*Cc*Vc/Vp - kpc*Cp)    d/dt(BM)    = ksyn_BM*STIM-(kdeg_BM*BM)     # Outputs and error models    C_ng_ml     = Cc*MW    C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)     BM_obs      = BM    BM_obs  ~ add(add_err_BM)  }) }"},{"path":"/articles/deployment.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Deployment and Customization","text":"Running ruminate simple straight forward using ruminate function: default presents modules standard format. However may wish deploy ruminate internal server customize .","code":"library(ruminate) ruminate()"},{"path":"/articles/deployment.html","id":"deployment","dir":"Articles","previous_headings":"","what":"Deployment","title":"Deployment and Customization","text":"deploy ruminate can create copy app based template package: Certain features app behave differently depending whether running locally deployed server. tell App deployed need create empty file named DEPLOYED directory deployed app file. two files (App.R DEPLOYED) placed direcory shiny server ruminate installed enough deploy basic app.","code":"file.copy(from = system.file(package=\"ruminate\",\"templates\", \"ruminate.R\"),            to   = \"App.R\") file.create(\"DEPLOYED\")"},{"path":"/articles/deployment.html","id":"customization","dir":"Articles","previous_headings":"","what":"Customization","title":"Deployment and Customization","text":"can open App.R file run app locally well. can also edit file customize app. includes default text presented, graphics might want, also color scheme. Beyond individual modules also customizable.","code":""},{"path":"/articles/deployment.html","id":"app-layout","dir":"Articles","previous_headings":"Customization","what":"App Layout","title":"Deployment and Customization","text":"app built around modules overall look introductory text controlled file. default app uses shinydashboard package. can look documentation package alter tabs, default skin color, module icons, etc. default package contains modules, can simply remove tabs modules want.","code":""},{"path":"/articles/deployment.html","id":"individual-modules","dir":"Articles","previous_headings":"Customization","what":"Individual Modules","title":"Deployment and Customization","text":"top App.R file locations module configuration files defined: controls different aspects module including text displayed, icons used, certain behaviors app, default values, etc. options described comments configuration files. use need first make copy specific configuration file. example, customize NCA module make copy NCA.yaml file: can make changes want myNCA.yaml file. need specify App.R file want use new configuration file. can thing modules well.","code":"formods.yaml  = system.file(package=\"formods\",  \"templates\",  \"formods.yaml\") ASM.yaml      = system.file(package=\"formods\",  \"templates\",  \"ASM.yaml\") UD.yaml       = system.file(package=\"formods\",  \"templates\",  \"UD.yaml\") DW.yaml       = system.file(package=\"formods\",  \"templates\",  \"DW.yaml\") FG.yaml       = system.file(package=\"formods\",  \"templates\",  \"FG.yaml\") NCA.yaml      = system.file(package=\"ruminate\", \"templates\",  \"NCA.yaml\") MB.yaml       = system.file(package=\"ruminate\", \"templates\",  \"MB.yaml\") CTS.yaml      = system.file(package=\"ruminate\", \"templates\",  \"CTS.yaml\") file.copy(from = system.file(package=\"ruminate\", \"templates\",  \"NCA.yaml\"),           to   = \"myNCA.yaml\") NCA.yaml      = \"myNCA.yaml\""},{"path":"/articles/model_builder.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Model Building","text":"model building module constructed around rxode2 package. allows load base model either model catalog user-create file. loaded user can modify model (change structure, parameter values, etc), make copy, etc. can upload model rxode2 format also NONMEM format. NONMEM file (control stream listing output) converted upload rxode2 using nonmem2rx package.","code":""},{"path":"/articles/model_builder.html","id":"model-catalog","dir":"Articles","previous_headings":"","what":"Model catalog","title":"Model Building","text":"model catalog built sources field configuration file. can include models nlmixr2lib model library. can also include user defined files. specified found, skipped. Edit MB yaml file alter .","code":""},{"path":"/articles/model_builder.html","id":"user-defined-models","dir":"Articles","previous_headings":"Model catalog","what":"User-defined models","title":"Model Building","text":"User-defined models can included catalog uploaded app. models can specified either NONMEM rxode2 formats. NONMEM models can control stream (good), listing output estimation (better), XML output estimation (best). use nlmixr2 model simply need function definition assigned object my_fcn. examples see: system.file(package=\"ruminate\", \"templates\", \"MB_model_rxode2.R\") system.file(package=\"ruminate\", \"templates\", \"MB_model_NONMEM.ctl\") Modules specified sources section configuration file. default configuration file look working directory App user_model.R user_model.ctl. rxode2 model can name user_model.R place app directory included catalog. Similarly NONMEM control stream can name user_model.ctl, included automatically. exist skipped. can create many entries want.","code":""},{"path":"/articles/model_builder.html","id":"configuration-file","dir":"Articles","previous_headings":"","what":"Configuration File","title":"Model Building","text":"","code":"# The MC field is a skeleton for constructing new modules MC:   # The prefix of object names created in the generated code   element_object_name: \"MB_obj\"   # This is a list of model sources. In the app the sources will be listed in   # the order shown here. This is also referred to as the model catalog. If   # the type is set to nlmixr2lib it will insert the models from nlmixr2lib.   # User defined models can be used as well. See the examples below for   # rxode2 and NONMEM models.   #   # - group: This is how models will be grouped in the selection pulldown.   # - name: Short name for the model: **NOTE that names need to be unique**.   # - description: Verbose description of the model.   # - type: Supported model types are \"nlmixr2lib\", \"rxode2\" or \"NONMEM\".   # - obj: For the rxode2 type you need to also specify the name of the object   #   that is created. This option is only used for the rxode2 type.   # - file: For both rxode2 and nlmixr2 you need to specify a character string   #   with a file path. This will be evaluated as an R command, so keep that   #   in mind. If the file is not found it will be skipped when the app loads.   sources:   - source:       group:       \"User-defined Models\"       name:        \"rxode2 User model\"       description: \"User-defined rxode2 model\"       type:        \"rxode2\"       obj:         \"my_fcn\"       file:        'file.path(getwd(),\"user_model.R\")'   - source:       group:       \"User-defined Models\"       name:        \"NONMEM User model\"       description: \"User-defined NONMEM model\"       type:        \"NONMEM\"       file:        'file.path(getwd(),\"user_model.ctl\")'   - source:       group:       \"Model Library\"       type:        \"nlmixr2lib\"       name:        \"nlmixr2 Model Library\" # The include section is optional. You can use this section to copy files # from the directory where you are running the app to the user directory to be # used in the app and when saved. The source and dest options can be a path # command like below or a double # quoted string: '\"/path/to/file\"' # The dest should be relative to user directory (think the root of the zip # file that is exported). See formods.yaml for examples. # # include: #   files: #   - file: #       source: 'file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\")' #       dest:   'file.path(\"config\",\"report.docx\")'   code: # module specific code options     packages: [ \"rxode2\", \"nonmem2rx\", \"nlmixr2lib\"]     readOnly: TRUE     mode:     r   # Controls what is shown in the compact ui mode:   compact:     code:    TRUE     clip:    TRUE   # This is where you put reporting options. The expected options are shown   # here, but module-specific options can also be added if needed:   reporting:     enabled: FALSE     # Priority of reporting relative to other modules     # (higher numbers are reported first)     priority: 1   # put different formatting options (widths, heights,   # shinyWidgets button sizes, etc) here.   formatting:     # Dimensions of code snippet box     code:       width:  800       height: 300     preview:       width:  \"800px\"       height: \"500px\"     input:     current_element:       width:  \"200px\"       tooltip: \"Change the current model.\"       tooltip_position: \"right\"     element_name:       width:  \"200px\"     button_clk_run:       size: \"sm\"       block: TRUE     button_clk_del:       size: \"sm\"       block: TRUE       tooltip: \"Delete the current model.\"       tooltip_position: \"right\"     button_clk_save:       size: \"sm\"       block: TRUE       tooltip: \"Save the name for the current model.\"       tooltip_position: \"right\"     button_clk_clip:       size: \"sm\"       block: TRUE       tooltip: \"Copy the code to generate the current model to the clipboard.\"       tooltip_position: \"right\"     button_clk_copy:       size: \"sm\"       block: TRUE       tooltip: \"Make a copy of the current model.\"       tooltip_position: \"right\"     button_clk_new:       size: \"sm\"       block: TRUE       tooltip: \"Create a new model.\"       tooltip_position: \"right\"       width: 100     catalog_selection:       width:   \"200px\"       truncate: 65       tooltip:  \"Select base model from the catalog.\"       tooltip_position: \"top\"     model_type_selection:       width: \"200px\"       tooltip: \"Select the type of the model being uploaded.\"       tooltip_position: \"right\"       choices:         rxode2: \"rxode2 function\"         NONMEM: \"NONMEM (XML, lst, or ctl)\"       default:   \"rxode2\"     upload_model_file:       width: \"400px\"     base_from:       size:    \"normal\"       status:  \"primary\"       tooltip: \"You can select the base model from a catalog or upload your own\"       tooltip_position: \"bottom\"       choices:         catalog: \"Model Catalog\"         user:    \"User-defined model\"       default: \"catalog\"     # The timescales here can be altered manually. This is the general format:     #   short_name:     #     conv: \"10\"     #     verb: \"A more verbose name\"     # Generally you dont want to  change what is already here in terms of     # these short name used. You can remove timescales you don't want to use     # or add timescales that you need but are not present.     #   -verb  You can change the verb field (what the user sees).     # The order of Appearance is the same order seen here. The default     # is the selected short name for the specified timescale     time_scales:       width: \"200px\"       default: \"weeks\"       tooltip: \"Choose the timescale of the model.\"       tooltip_position: \"top\"       choices:         months:           conv: \"60*60*24*4\"           verb: \"Months\"         weeks:           conv: \"60*60*24*7\"           verb: \"Weeks\"         days:           conv: \"60*60*24\"           verb: \"Days\"         hours:           conv: \"60*60\"           verb: \"Hours\"         min:           conv: \"60\"           verb: \"Minutes\"         sec:           conv: \"1\"           verb: \"Seconds\"   labels:     current_element: NULL # \"Select model\"     element_name :   NULL # \"model name\"     upload_model_file:    NULL     base_from:            NULL     catalog_selection:    NULL     model_type_selection: NULL     time_scale:           NULL     catalog_empty:        \"No models were found, the catalog is empty\"     save_btn:             \"Save\"     clip_btn:             \"Code\"     copy_btn:             \"Copy\"     del_btn:              \"Delete\"     new_btn:              \"New\"     building_model:       \"Building model, be patient.\"     element_name_diff:    \"The model name has changed.\"     model_code_diff:      \"Manual changes have been made to the model.\"     save_change_detected: \"You need to click on the save button for these changes to take effect.\"     head_base_model:      \"Starting model\"     head_model_code:      \"Model code\"     head_time_scale:      \"Model time-scale\"   errors:     no_model_found:             \"No model was found.\"     base_model_build_failed:    \"The base model build failed.\"     manual_model_update_failed: \"Manual model update failed.\"     user_file_upload_failed:    \"User-defined model failed.\"     nlmixr2lib_not_found:       \"The nlmixr2lib package was not found. This library will not be available.\"     selected_id_bad_list:       \"Unable to find a list for the selected component.\"     selected_id_bad_row:        \"Selected component should have 1 row.\"   tooltips:     # Set to FALSE to disable tool tips for this module     include: TRUE     elements: \"Show model components\"     show_code: \"Show model code\"     url_rxode: \"https://nlmixr2.github.io/rxode2/articles/rxode2-syntax.html\"     url_model_types: \"https://ruminate.ubiquity.tools/articles/model_builder.html#model-catalog\"     components:       input_tip: \"My tool tip\""},{"path":"/articles/noncompartmental_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Noncompartmental Analysis","text":"Non-compartmental analysis (NCA) simple quick method evaluating exposure drug. allows evaluate things like linearity vivo exposure. illustrate consider antibody given subcutaneous injection. actual exposure profile patient might experience given solid black line left panel. don’t yet ability sample continuous fashion. might observer given blue points.  Generally NCA determine following directly data: Cmax - Maximum observed concentration (units=concentration) Tmax - time maximum concentration observed (units=time) AUC - area curve (\\(units=time \\times concentration\\)) AUMC - area first moment curve (\\(units=time^2 \\times concentration\\)) properties based observational data. Cmax Tmax certainly actual maximum concentration long sample judiciously give us good approximation. Similarly, calculated AUC AUMC different actual values. calculate areas need dig back calculus text books trapezoid method. Basically sampling interval trapezoid area calculated added n samples: \\[ \\begin{eqnarray} AUC  &=& \\int_0^{t_f} Cdt &\\approx  \\sum_{=1}^{n-1}{\\frac{C_i+C_{+1}}{2}\\times (t_{+1}-t_{})}  \\\\ AUMC &=& \\int_0^{t_f} t\\times Cdt &\\approx  \\sum_{=1}^{n-1}{\\frac{t_iC_i+t_{+1}C_{+1}}{2}\\times (t_{+1}-t_{})} \\end{eqnarray} \\] can done Excel pretty easily. Depending data analysis properties can calculated. example can calculate clearance, mean residence time, steady-state volume distribution terminal half-life: Clearance: \\(CL = \\frac{Dose}{AUC}\\) Mean residence time: \\(MRT = \\frac{AUMC}{AUC}\\) Steady state volume distribution: \\(V_{ss} = MRT \\times CL\\) Half-life: Terminal slope natural log data Properties like AUC AUMC can also calculated using extrapolation last time point infinity account data beyond observations hand. subsequent values clearance, volumes distribution, etc can also change extrapolation. lot nuance associated calculations, good rely software focuses type analysis. PKNCA package developed mind. ruminate app provides interface functionality found PKNCA generate code can use get started PKNCA.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"data-format","dir":"Articles","previous_headings":"","what":"Data format","title":"Noncompartmental Analysis","text":"run NCA need data formatted specific way. names columns can defined user. columns required, others optional, others required depending user wants specify dosing. See general description columns followed information dosing inferred: Subject ID: Unique id subject dataset (required) Time: Observation time since first dose (required) Nominal Time: Time since recent dose (optional, needed determine dosing columns) Dose: Amount dose (required) Concentration: Observed concentration (NA missing values, required) Route: Dosing route (intravascular extravascular, required) Grouping: columns used grouping (optional) Dose Duration: Duration infusion (optional) Dosing Cycle Period: recent dose cycle period (optional, needed dosing columns) Event ID: EVID column NONMEM used distinguish dosing observation records (optional, needed dosing rows) Analyte: Used multiple analytes present dataset (optional) Note Dose Concentration need mass units.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"dosing-information","dir":"Articles","previous_headings":"Data format","what":"Dosing information","title":"Noncompartmental Analysis","text":"Dosing extracted datasets, can incorporated two ways. One dosing information stored columns dataset. simplest straight forward way storing dosing information. method dosing specified individual records (rows) dataset (data formatted NONMEM, can use ).","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"determining-dosing-from-columns","dir":"Articles","previous_headings":"Data format > Dosing information","what":"Determining dosing from columns","title":"Noncompartmental Analysis","text":"Beyond required columns infer dosing columns must also following columns defined: Dose, Dosing Cycle Period, Nominal Time.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"determining-dosing-from-rows-nonmem-format","dir":"Articles","previous_headings":"Data format > Dosing information","what":"Determining dosing from rows (NONMEM format)","title":"Noncompartmental Analysis","text":"dataset formatted NONMEM can extract dosing rows contain dosing records. case expected following columns dataset: Dose (AMT NONMEM) Event ID (EVID NONMEM). notes dosing information. interval want calculate dose-dependent parameters, need provide information dosing intervals. multiple dose setting intervals enough PK data run NCA , necessary provide dosing information intervals. example four weekly doses intensive sampling weeks 1 4 peak trough weeks 2 3, provide dosing information weeks 2 3. However certain parameters PKNCA provide dosing intermediate sampling information provided (e.g. time steady-state). interested parameters intervals intensive sampling, don’t need worry dosing information intervals.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"nca-analysis-intervals","dir":"Articles","previous_headings":"Data format","what":"NCA analysis intervals","title":"Noncompartmental Analysis","text":"select interval analysis, necessary observations beginning end interval. observations dosing times (e.g. dose time zero first observation 15 minutes post-dose) want use dosing time beginning interval, can observation BQL missing (NA) dosing time (zero).","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"nca-parameters","dir":"Articles","previous_headings":"Data format","what":"NCA parameters","title":"Noncompartmental Analysis","text":"following shows different NCA parameters can calculated. PKNCA parameter value used PKNCA, App Parameter Name value shown app.","code":""},{"path":"/articles/noncompartmental_analysis.html","id":"configuration-file","dir":"Articles","previous_headings":"","what":"Configuration File","title":"Noncompartmental Analysis","text":"","code":"MC:   code: # module specific code options     packages: [\"PKNCA\", \"dplyr\", \"stringr\", \"formods\", \"ruminate\", \"onbrand\"]     readOnly: TRUE     mode:     r     preamble: |-       # Non-compartmental analysis ----------------------------------------------   compact:     code:    TRUE     clip:    TRUE   reporting:     enabled: TRUE     # Text format determines how text will be rendered. It can be either \"md\"     # for Markdown or \"text\" for plain text. This will also control things     # like table headers.     text_format: \"md\"     # Priority of reporting relative to other modules (higher numbers are     # reported first)     priority: 10     # The order of the figure and table ids (e.g. fg_ind_obs) represents the     # order in which figures and tables will be reported. Each id should have     # the following optoins:     #     #   caption_single      Caption used when a table or figure needs only a     #                       single page.     #     #   caption_multiple    Caption used when a table or figure spans multiple     #                       pages.     #     #   title_single        Slide title used when a table or figure needs only a     #                       single page.     #     #   title_multiple      Slide title used when a table or figure spans multiple     #                       pages.     #     #   sub_title_single    Slide sub_title used when a table or figure needs only a     #                       single page.     #     #   sub_title_multiple  Slide sub_title used when a table or figure spans multiple     #                       pages.     #     # The following placeholders can be used bu surroudning them with ===. For     # example to include the total number of figures you would use     # ===FIGTOT===:     #     # NCADESC               Description of the NCA analysis provided by the     #                       user in the UI (tables and figures).     #     # FIGNUM                Number of the current figure for figures that span     #                       multiple pages (figures only).     #     # FIGTOT                Total number of figures for figures that span     #                       multiple pages (figures only).     #     # TABNUM                Number of the current table for tables that span     #                       multiple pages (tables only).     #     # TABTOT                Total number of tables for tables that span multiple     #                       pages (tables only).     #     #     figures:       fg_ind_obs:         caption_single:     \"===NCADESC=== (Individual Observations)\"         caption_multiple:   \"===NCADESC=== (Individual Observations,  ===FIGNUM=== of ===FIGTOT===)\"         title_single:       \" Individual Observations\"         title_multiple:     \" Individual Observations\"         sub_title_single:   \"===NCADESC===\"         sub_title_multiple: \"===NCADESC=== (===FIGNUM=== of ===FIGTOT===)\"     tables:       tb_ind_params:         caption_single:   \"===NCADESC=== (Individual Parameters)\"         caption_multiple: \"===NCADESC=== (Individual Parameters, ===TABNUM=== of ===TABTOT===)\"       tb_ind_obs:         caption_single:   \"===NCADESC===, Individual Data\"         caption_multiple: \"===NCADESC===, Individual Data (===TABNUM=== of ===TABTOT===)\"     # Default number of significant digits     digits: 3     # When reporting units the * will be replaced with this value:     mult_str: \"⋅\"     # You can use unicode here like this \"∞\"     # How infinity is represented in reports like table headers     # to change inf in parameters you need to change them for the individual     # parameters below.     infinity:    \"inf\"     not_calc:    \"NC\"     not_sampled: \"NS\"     blq:         \"BLQ\"     # Here we list all the notes that can be present in tables. You need an     # entry for whatever you put in not_calc and blq above here.     notes:       NC: \"not calculated\"       NS: \"not sampled\"       BLQ: \"below the level of quantification\"   notifications:     ana_add_int_success: \"Interval added: ===DETAILS===\"   # Put different formatting options (widths, heights,   # shinyWidgets button sizes, etc) here.   formatting:     # Dimensions of code snippet box     code:       width:  800       height: 300     # Dimensions of the notes text area     notes:       width:  \"520px\"       height: \"75px\"     # Width of textInput and selectInput UI elements in the     # nca configuration     nca_config_option:       width: 150     # Width of the figure preview (must be numeric)     # The options nrow and ncol refer to the default number of rows and     # columns when faceting. The options nrow_opt and nrow_col are values the     # user can select.     #     preview:       width:           \"100%\"       height:          \"600px\"       nrow:            4       ncol:            4       nrow_opt:        [ 1, 2, 3, 4, 5, 6, 7, 8 ]       ncol_opt:        [ 1, 2, 3, 4, 5, 6, 7, 8 ]       logy:            TRUE       fig_type:        \"report\"       tab_type:        \"report\"     # These apply to the intervals table     dose_from:       default:          \"cols\"       tooltip:          \"Dosing information can be found in either columns (column for dose, dose cycle, nominal time) or rows (one row for each dose event distinguished by an event ID (EVID) column).\"       tooltip_position: \"top\"     intervals:       width:  600       height: 175     intervals_start:       width:  50     intervals_stop:       width:  50     intervals_np_text:       width:  375     intervals_delete:       width:  75     button_ana_run:       size: \"sm\"       block: TRUE     button_ana_del:       size: \"sm\"       block: TRUE       tooltip: \"Delete the current analysis.\"       tooltip_position: \"right\"     button_ana_save:       size: \"sm\"       block: TRUE       tooltip: \"Save the caption and notes and apply the data view for the current analysis.\"       tooltip_position: \"right\"     button_ana_clip:       size: \"sm\"       block: TRUE       tooltip: \"Copy the code to generate the current analysis to the clipboard.\"       tooltip_position: \"right\"     button_fg_save:       size: \"sm\"       block: TRUE      #tooltip: \"tooltip\"      #tooltip_position: \"right\"     button_tb_save:       size: \"sm\"       block: TRUE      #tooltip: \"tooltip\"      #tooltip_position: \"right\"     button_ana_copy:       size: \"sm\"       block: TRUE       tooltip: \"Make a copy of the current analysis.\"       tooltip_position: \"right\"     button_ana_new:       size: \"sm\"       block: TRUE       tooltip: \"Create a new analysis.\"       tooltip_position: \"right\"     button_ana_add_int:       size: \"sm\"       block: TRUE     button_ana_use_scenario:       size: \"sm\"       block: TRUE     check_fg_ind_obs:       width: 75     select_ana_interval_range:       width:  200     text_ana_interval_start:       width: 75     text_ana_interval_stop:       width: 75     select_current_view:       width: 200     select_ana_fig_view:       width: 250     select_ana_tab_view:       width: 250     select_ana_source_sampling:       width: 150     select_ana_nca_parameters:       width: 150     select_ana_col_id:       width: 150       tooltip:  \"Unique id for each subject in the dataset (required)\"       tooltip_position: \"bottom\"     select_ana_col_dose:       width: 150       tooltip: \"Amount of dose (required)\"       tooltip_position: \"bottom\"     select_ana_col_dur:       width: 150       tooltip: \"Duration of infusion (optional)\"       tooltip_position: \"bottom\"     select_ana_col_conc:       width: 150       tooltip: \"Observed concentration (NA for missing values, required)\"       tooltip_position: \"bottom\"     select_ana_col_route:       width: 150       tooltip: \"Dosing route (intravascular  or extravascular, required)\"       tooltip_position: \"bottom\"     select_ana_col_time:       width: 150       tooltip: \"Observation time since the first dose (required)\"       tooltip_position: \"bottom\"     select_ana_col_ntime:       width: 150       tooltip: \"Time since the most recent dose (optional, needed to determine dosing from columns)\"       tooltip_position: \"bottom\"     select_ana_col_group:       width: 150       tooltip: \"Other columns used for grouping (optional)\"       tooltip_position: \"bottom\"     select_ana_col_analyte:       width: 150       tooltip: \"Used if multiple analytes are present in the dataset (optional)\"       tooltip_position: \"bottom\"     select_ana_col_evid:       width: 150       tooltip: \"EVID column from NONMEM used to distinguish dosing and observation records (optional, needed for dosing from rows)\"       tooltip_position: \"bottom\"     select_ana_col_cycle:       width: 150       tooltip: \"The most recent dose cycle or period (optional, needed for dosing from columns)\"       tooltip_position: \"bottom\"     select_ana_units_dose:       width: 150     select_ana_units_time:       width: 150     select_ana_units_conc:       width: 150     select_ana_units_amt:       width: 150     select_ana_scenario:       width: 150     select_fg_ind_obs_ncol:       width: 50     select_fg_ind_obs_nrow:       width: 50     select_fg_ind_obs_page:       width: 100     select_tb_page:       width: 100   errors:     nca_run_filed: \"Unable to run PKNCA. Please, see below for details.\"     bad_input: \"Input error message\"     nca_no_fig: \"There are no figures to display. You must run the analysis first\"     nca_no_tab: \"There are no tables to display. You must run the analysis first\"   tooltips:     # Set to FALSE to disable tool tips for this module     include: TRUE     notes:             \"Optional analysis notes that will be carried over and used in other elements (e.g. reporting).\"     ana_key:           \"Unique description used for referencing analysis and in other elements (e.g. reporting).\"     show_code:         \"Show analysis code\"     nca_intervals:     \"Current NCA intervals\"     # Set urls to NULL to disable     url_dosing:        \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#dosing-information\"     url_data:          \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#data-format\"     url_intervals:     \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#nca-analysis-intervals\"     url_parameters:    \"https://ruminate.ubiquity.tools/articles/noncompartmental_analysis.html#nca-parameters\"     ph:       text_ana_interval_start: 0       text_ana_interval_stop:  \"number or Inf\"       notes:                   \"Optional NCA notes that will be carried over and used in other elements (e.g. reporting).\"   # This controls the figure and table selection for an analysis. The order of   # the keys (e.g., tb_ind_params, tb_sum_params, etc) controls the   # order they are dispalyed in the selector and the text controls what's show   # in the interface.   figures:     fg_ind_obs:       choice:  \"Individual Profiles\"       subtext: \"Faceted view of individual time-course\"   tables:     tb_ind_params:       choice:  \"Individual NCA Parameters\"       subtext: \"Listings of subject-level NCA parmaeters\"     tb_sum_params:       choice:  \"Summarized NCA Parameters\"       subtext: \"Listings of NCA parmaeters summarized by group/analyte\"     tb_ind_obs:       choice:  \"Individual Data\"       subtext: \"Listings of subject-level time-course data\"     tb_nca_raw:       choice:  \"Raw NCA Output\"       subtext: \"Output from PKNCA with requested and dependent parameters\"   labels:     ana_key:                         \"title/caption\"     new_ana:                         \"New\"     run_ana:                         \"Run NCA\"     save_ana:                        \"Save\"     del_ana:                         \"Delete\"     copy_ana:                        \"Copy\"     clip_ana:                        \"Code\"     fg_save:                         \"Apply changes\"     tb_save:                         \"Apply changes\"     curr_anas_none:                  \"No figures yet created\"     no_dataset:                      \"No datasets available to analyze. You need to load a dataset to use this module.\"     ana_use_scenario:                \"Analysis Template\"     ana_add_int:                     \"Add/Update Interval\"     no_intervals:                    \"No intervals added yet\"     panel_analysis_opts:             \"Analysis Details\"     panel_figures:                   \"Figures\"     panel_tables:                    \"Tables\"     panel_nca_config:                \"NCA Options\"     text_ana_interval_start:         \"Start\"     text_ana_interval_stop:          \"End\"     select_ana_interval_range:       \"Interval Range\"     # Headings used in the UI     head_intervals:                  \"Analysis Time Intervals\"     head_intervals_current:          \"Current Intervals\"     head_analysis_template:          \"Use Analysis Template\"     head_intervals_create:           \"Create an Interval\"     head_dose_from:                  \"Dosing Information\"     head_col_mapping:                \"Identify Columns\"     head_col_mapping_required:       \"Required\"     head_col_mapping_optional:       \"Optional\"     head_run_analysis:               \"Run Analysis\"     head_units:                      \"Units\"     select_current_view:             \"Data from\"     select_current_ana:              \"Current NCA\"     select_ana_scenario:             NULL  # \"Type of Analysis:\"     select_ana_col_id:               \"Subject ID\"     select_ana_col_time:             \"Time\"     select_ana_col_ntime:            \"Nominal Time\"     select_ana_col_dose:             \"Dose\"     select_ana_col_dur:              \"Dose Duration\"     select_ana_col_conc:             \"Concentration\"     select_ana_col_route:            \"Route\"     select_ana_col_group:            \"Other Grouping\"     select_ana_col_cycle:            \"Dosing Cycle or Period\"     select_ana_col_evid:             \"Event ID\"     select_ana_col_analyte:          \"Analyte\"     select_ana_units_time:           \"Time\"     select_ana_units_dose:           \"Dose\"     select_ana_units_conc:           \"Concentration\"     select_ana_units_amt:            \"Amount\"     select_ana_nca_parameters:       \"Parameters\"     select_fg_ind_obs_nrow:          \"Rows\"     select_fg_ind_obs_ncol:          \"Columns\"     select_fg_ind_obs_page:          \"Show Page\"     select_tb_page:                  \"Show Page\"     check_fg_ind_obs_logy:           \"Log 10 y-scale\"     switch_ana_include_units:        \"Include Units\"     switch_ana_fig:                  NULL     switch_ana_fig_interactive:      \"Interactive\"     switch_ana_fig_report:           \"Report Preview\"     switch_ana_dose_from:            \"Dosing Found In\"     switch_ana_dose_from_rows:       \"Rows\"     switch_ana_dose_from_cols:       \"Columns\"     switch_ana_tab:                  NULL     switch_ana_tab_interactive:      \"Interactive\"     switch_ana_tab_report:           \"Report Preview\"     select_ana_source_sampling:      \"Data Sampling\"     busy:       run_nca:                       \"Running NCA be patient.\"     PKNCA_raw:       ID:                            \"Subject ID\"       start:                         \"Initial time of the analysis interval.\"       end:                           \"Final time of the analysis interval.\"       PPTESTCD:                      \"Parameter name from PKNCA.\"       PPORRES:                       \"parameter value.\"       exclude:                       \"Reason for parameter exclusion (NA when parameter was calculated).\"       PPORRESU:                      \"Parameter units.\"   # For details on the PKNCA options see this vignette:   # https://billdenney.github.io/pknca/articles/v40-options-for-controlling-PKNCA.html   # This section contains information about different options, metadata about   # them, default values, etc. Using this, the options UI for an analysis is   # generated automatically. Each configuration option has the following:   #   # group        - This is how configuration options will be grouped in the app   # label        - Short textual description   # tooltip      - More detailed description of the option and what goes into it.   #                Set to NULL to not display a tool tip.   # type         - List of possible types. For example if you have \"numeric\" then   #                \"character\" the value will be evaluated as numeric, if this is   #                NA then it will be evaluated as character. Allowed types:   #                  - numeric   #                  - character   #                  - mixed (numeric and character)   #                  - logical (TRUE or FALSE)   # value        - Default value in the UI   # options      - If set to NULL a text input box will be created for this option,   #                if there are options then a selection box will be created.   # pknca_option - The actual option set with PKNCA   nca_config:     adj_r_squared_factor:       group:         General       # BILL I need a better label for this       label:         \"r² (Adj) factor\"       tooltip:       \"The adjusted r² for the calculation of λz has this factor times the number of data points added to it. It allows for more data points to be preferred in the calculation of half-life.\"       type:          \"numeric\"       value:         0.0001       options:       NULL       pknca_option:  adj.r.squared.factor     max_missing:       group:         General       label:         \"Max missing (frac)\"       tooltip:       \"The maximum fraction of the data that may be missing (‘NA’) to calculate summary statistics.\"       type:          \"numeric\"       value:         0.5       options:       NULL       pknca_option:  max.missing     auc_method:       group:         AUC       label:         Method       tooltip:       \"The method used to calculate the AUC and related statistics.\"       type:          \"character\"       value:         \"lin up/log down\"       options:       [\"lin up/log down\", \"linear\"]       pknca_option:  auc.method     conc_na:       group:         Missing and BLQ Values       label:         Missing values       tooltip:       \"Determines how missing (NA) values are generally handled. Can be either drop or a numeric value to use. \"       type:          \"mixed\"       value:         \"drop\"       options:       NULL       pknca_option:  conc.na     conc_blq_first:       group:         Missing and BLQ Values       label:         First value BLQ       tooltip:       \"Indicates what to do if the first observation is BLQ. It can either be drop, keep, or a numeric value to use.\"       type:          \"mixed\"       value:         \"keep\"       options:       NULL       pknca_option:  conc.blq$first     conc_blq_middle:       group:         Missing and BLQ Values       label:         Middle values BLQ       tooltip:       \"Indicates what to do if a value between the first and last observations is BLQ. It can either be drop, keep, or a numeric value to use.\"       type:          \"mixed\"       value:         \"drop\"       options:       NULL       pknca_option:  conc.blq$middle     conc_blq_last:       group:         \"Missing and BLQ Values\"       label:         \"Last Value BLQ\"       tooltip:       \"Indicates what to do if the last observation is BLQ. It can either be drop, keep, or a numeric value to use.\"       type:          \"mixed\"       value:         \"keep\"       options:       NULL       pknca_option:  conc.blq$last     first_tmax:       group:         \"General\"       label:         \"Multiple tmax use first\"       tooltip:       \"If there is more than one concentration equal to Cmax, should the time selected for Tmax be the first value? If TRUE, the first will be selected. If FALSE, the last will be selected.\"       type:          \"logical\"       value:         TRUE       options:       [TRUE, FALSE]       pknca_option:  first.tmax     allow_tmax_in_half_life:       group:         \"Half-Life\"       label:         \"Allow tmax in half-life\"       tooltip:       \"Should the concentration and time at Tmax be allowed in the half-life calculation? TRUE is yes and FALSE is no.\"       type:          \"logical\"       value:         FALSE       options:       [TRUE, FALSE]       pknca_option:  allow.tmax.in.half.life     min_hl_points:       group:         \"Half-Life\"       label:         \"Min points\"       tooltip:       \"What is the minimum number of points required to calculate half-life?\"       type:          \"numeric\"       value:         3       options:       [ 2, 3, 4, 5 ]       pknca_option:  min.hl.points     min_span_ratio:       group:         \"Half-Life\"       label:         \"Min span ratio\"       # BILL this is a bit cryptic       tooltip:       \"What is the minimum span ratio required to consider a half-life valid?\"       type:          \"logical\"       value:         2       options:       NULL       pknca_option:  min.span.ratio     min_hl_r_squared:       group:         \"Half-Life\"       label:         \"Min r² value\"       tooltip:       \"What is the minimum r² value to consider a half-life calculation valid?\"       type:          \"numeric\"       value:         0.9       options:       NULL       pknca_option:  min.hl.r.squared     max_aucinf_pext:       group:         \"AUC\"       label:         \"Max allowed extrap (%)\"       tooltip:       \"What is the maximum percent extrapolation to consider an AUC∞ valid?\"       type:          \"numeric\"       value:         20       options:       NULL       pknca_option:  max.aucinf.pext   # Prefix used to create NCA analysis objects in generated code   nca_object_name: \"myANA_\"   # The following patterns are used when detecting columns in your dataset   # automatically. You can create a multiple patterns. They will be processed   # in order from left to right. The first pattern to match will return that   # column. For example if you set id to:   #   # id:   [\"^(?i)id$\",    \"(?i)subject\"]   #   # It will match any column that is id. The (?i) is case insensitive. But it   # wont match IDs for example because it looks for something that starts with   # i (^) and ends with d ($).   #   # If that fails it will look for any column with the text 'subject' in it   # regardless of case. So it would match SUBJECT, subject, Subjects,   # subject id, etc.   #   # If you set it to NULL no attempt will be made to detect the column.   detect_col:     id:      [\"^(?i)id$\",    \"(?i)subject\",    \"(?i)animal\"]     time:    [\"^(?i)time\",   \"(?i)ntime\"  ]     ntime:   [\"^(?i)ntime\",  \"(?i)time\"   ]     conc:    [\"^(?i)conc\",   \"^(?i)dv$\",      \"^(?i)pcstresn$\", \"^(?i)lbstresn$\", \"^(?i)AVAL(N|NUM)?$\"]     dose:    [\"(?i)dose\",    \"^(?i)ecdose?$\", \"^(?i)exdose?$\"]     evid:    [\"(?i)evid\"]     route:   [\"(?i)route\"]     cycle:   [\"(?i)cycle\",   \"(?i)dose_num\", \"(?i)period\"]     group:   NULL     analyte: NULL     dur:     NULL   # Routes may be specified in the dataset in a more detailed or convenient   # format than what is needed for analysis. Here you can specify patterns   # that can be used to detect routes. For details on the regular expressions   # that can be used see the detect_col section above. In general you can use   # the format of:   #   #   myroute: [\"regex1\", \"regex2\"]   #   # This will assign the route to 'myroute' in the ROUTE column if it matches   # regex1 or regex2.   #   detect_route:     intravascular: [\"^(?i)iv$\"]     extravascular: [\"^(?i)sc$\", \"^(?i)oral\"]   # These are default analysis scenarios that are shown in the App. They will   # be shown in the order defined. Each scenario has the following attributes:   #   - description: Free form textual description shown to the user   #   - nca_parameters: List of nca parameters to be calculated   #   - sampling: Can be either \"serial\" or \"sparse\"   #   BILL: Get relevant scenarios from bill here   ana_scenarios:     sd_iv:       description: \"Single Dose (IV)\"       nca_parameters: [\"cmax\", \"auclast\", \"aucinf.obs\"]       sampling:   \"serial\"       intervals:         - row:             start:         0             stop:          Inf             nca_parameters: [\"aucinf.obs\"]         - row:             start:         0             stop:          21             nca_parameters: [\"cmax\", \"auclast\"]     md_iv:       description: \"Multiple Dose IV\"       nca_parameters: [\"cmax\", \"auclast\", \"aucinf.obs\"]       sampling:   \"serial\"       intervals:         - row:             start:         0             stop:          Inf             nca_parameters: [\"cmax\", \"auclast\",\"aucinf.obs\"]     sd_iv_sparse:       description: \"Single Dose (IV) Sparse Sampling\"       nca_parameters: [\"cmax\", \"sparse_auclast\"]       sampling:   \"sparse\"       intervals:         - row:             start:          0             stop:           Inf             nca_parameters: [\"cmax\", \"sparse_auclast\"]     md_iv_sparse:       description: \"Multiple Dose IV Sparse Sampling\"       nca_parameters: [\"cmax\", \"sparse_auclast\"]       sampling:   \"sparse\"       intervals:         - row:             start:          0             stop:           Inf             nca_parameters: [\"cmax\", \"sparse_auclast\"]   # This is the default shown when the app loads or a new analysis is created   ana_scenario_def: \"sd_iv\"   # Here are all of the available NCA parameters. Each field is the name of   # the parameter used in PKNCA. See the table in this vignette for the   # available parameters:   #   # https://cran.r-project.org/web/packages/PKNCA/vignettes/v03-selection-of-calculation-intervals.html   #   # Each of parameter has the following   #   #  - description: This is a verbose description of the parameter. If set to NULL the   #                 description from PKNCA will be used. Set it to other text to   #                 overwrite it. This description will also be used in exports as   #                 well.   #   #  - text:        Textual description to show the user. If set to NULL it   #                 will use the text form PKCNA.   #   #  - md:          Markdown formatted value of text.   #   #  - group:       This is how the paramters will be grouped in the UI that   #                 is generated.   #   # To limit the options the user sees you simply need to comment out the   # parameters you do not want in the App. Alterantively you can use the   # grouping below to have the most common parameters together at the top and   # the remaining in case they are needed.   #   # BILL can you go through the text and md elements below and make any   # changes you think make sense. For example is there a better way to label   # the parameter aucint.inf.obs.dose :).   #   nca_parameters:     aucall:       description: NULL       text:        NULL       md:          \"AUC~all~\"       group:       \"Common Parameters\"     adj.r.squared:       description: NULL       text:        \"r² (Adj)\"       md:          \"Adj. R^2^\"       group:       \"Other\"     ae:       description: NULL       text:        NULL       md:          \"AE\"       group:       \"Other\"     aucall.dn:       description: NULL       text:        NULL       md:          \"AUC~all,dn~\"       group:       \"Other\"     aucinf.obs:       description: NULL       text:        NULL       md:          \"AUC~inf,obs~\"       group:       \"Other\"     aucinf.obs.dn:       description: NULL       text:        NULL       md:          \"AUC~inf,obs,dn~\"       group:       \"Other\"     aucinf.pred.dn:       description: NULL       text:        NULL       md:          \"AUC~inf,pred,dn~\"       group:       \"Other\"     aucint.all:       description: NULL       text:        NULL       md:          \"AUC~int,all~\"       group:       \"Other\"     aucint.all.dose:       description: NULL       text:        NULL       md:          \"AUC~int,all,dose~\"       group:       \"Other\"     aucint.inf.obs:       description: NULL       text:        NULL       md:          \"AUC~int,inf,obs~\"       group:       \"Other\"     aucint.inf.obs.dose:       description: NULL       text:        NULL       md:          \"AUC~int,inf,obs,dose~\"       group:       \"Other\"     aucint.inf.pred:       description: NULL       text:        NULL       md:          \"AUC~int,inf,pred~\"       group:       \"Other\"     aucint.inf.pred.dose:       description: NULL       text:        NULL       md:          \"AUC~int,inf,pred,dose~\"       group:       \"Other\"     aucint.last:       description: NULL       text:        NULL       md:          \"AUC~int,last~\"       group:       \"Other\"     aucint.last.dose:       description: NULL       text:        NULL       md:          \"AUC~int,last,dose~\"       group:       \"Other\"     aucivall:       description: NULL       text:        NULL       md:          \"AUC~iv,all~\"       group:       \"Other\"     aucivinf.obs:       description: NULL       text:        NULL       md:          \"AUC~iv,inf,obs~\"       group:       \"Other\"     aucivinf.pred:       description: NULL       text:        NULL       md:          \"AUC~iv,inf,pred~\"       group:       \"Other\"     aucivint.all:       description: NULL       text:        NULL       md:          \"AUC~iv,int,all~\"       group:       \"Other\"     aucivint.last:       description: NULL       text:        NULL       md:          \"AUC~iv,int,last~\"       group:       \"Other\"     aucivlast:       description: NULL       text:        NULL       md:          \"AUC~iv,last~\"       group:       \"Other\"     aucivpbextall:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,all~\"       group:       \"Other\"     aucivpbextinf.obs:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,obs~\"       group:       \"Other\"     aucivpbextinf.pred:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,pred~\"       group:       \"Other\"     aucivpbextint.all:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,all~\"       group:       \"Other\"     aucivpbextint.last:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,inf,last~\"       group:       \"Other\"     aucivpbextlast:       description: NULL       text:        NULL       md:          \"AUC~iv,pbe,last~\"       group:       \"Other\"     auclast:       description: NULL       text:        NULL       md:          \"AUC~last~\"       group:       \"Other\"     auclast.dn:       description: NULL       text:        NULL       md:          \"AUC~last,dn~\"       group:       \"Other\"     aucpext.obs:       description: NULL       text:        NULL       md:          \"AUC~pext,obs~\"       group:       \"Other\"     aucpext.pred:       description: NULL       text:        NULL       md:          \"AUC~pext,pred~\"       group:       \"Other\"     aumcall:       description: NULL       text:        NULL       md:          \"AUMC~all~\"       group:       \"Other\"     aumcall.dn:       description: NULL       text:        NULL       md:          \"AUMC~all,dn~\"       group:       \"Other\"     aumcinf.obs:       description: NULL       text:        NULL       md:          \"AUMC~inf,obs~\"       group:       \"Other\"     aumcinf.obs.dn:       description: NULL       text:        NULL       md:          \"AUMC~inf,obs,dn~\"       group:       \"Other\"     aumcinf.pred.dn:       description: NULL       text:        NULL       md:          \"AUMC~inf,pred,dn~\"       group:       \"Other\"     aumclast:       description: NULL       text:        NULL       md:          \"AUMC~last~\"       group:       \"Other\"     aumclast.dn:       description: NULL       text:        NULL       md:          \"AUMC~last,dn~\"       group:       \"Other\"     c0:       description: NULL       text:        NULL       md:          \"C~0~\"       group:       \"Common Parameters\"     cav:       description: NULL       text:        NULL       md:          \"C~ave~\"       group:       \"Other\"     cav.dn:       description: NULL       text:        NULL       md:          \"C~ave,dn~\"       group:       \"Other\"     ceoi:       description: NULL       text:        NULL       md:          \"C~EOI~\"       group:       \"Other\"     cl.all:       description: NULL       text:        NULL       md:          \"CL\"       group:       \"Common Parameters\"     cl.last:       description: NULL       text:        NULL       md:          \"CL~last~\"       group:       \"Common Parameters\"     cl.obs:       description: NULL       text:        NULL       md:          \"CL~obs~\"       group:       \"Other\"     cl.pred:       description: NULL       text:        NULL       md:          \"CL~pred~\"       group:       \"Other\"     clast.obs:       description: NULL       text:        NULL       md:          \"CL~last,obs~\"       group:       \"Other\"     clast.pred:       description: NULL       text:        NULL       md:          \"CL~last,pred~\"       group:       \"Other\"     clast.pred.dn:       description: NULL       text:        NULL       md:          \"CL~last,pred,dn~\"       group:       \"Other\"     clr.last:       description: NULL       text:        NULL       md:          \"CL~R,last~\"       group:       \"Other\"     clr.obs:       description: NULL       text:        NULL       md:          \"CL~R,obs~\"       group:       \"Other\"     clr.pred:       description: NULL       text:        NULL       md:          \"CL~R,pred~\"       group:       \"Other\"     cmax:       description: NULL       text:        NULL       md:          \"C~max~\"       group:       \"Common Parameters\"     cmax.dn:       description: NULL       text:        NULL       md:          \"C~max,dn~\"       group:       \"Other\"     cmin:       description: NULL       text:        NULL       md:          \"C~min~\"       group:       \"Other\"     cmin.dn:       description: NULL       text:        NULL       md:          \"C~min,dn~\"       group:       \"Other\"     ctrough:       description: NULL       text:        NULL       md:          \"C~tr~\"       group:       \"Other\"     ctrough.dn:       description: NULL       text:        NULL       md:          \"C~tr,dn~\"       group:       \"Other\"     deg.fluc:       description: NULL       text:        NULL       md:          \"dgf\"       group:       \"Other\"     end:       description: NULL       text:        NULL       md:          \"t~end~\"       group:       \"Other\"     f:       description: NULL       text:        NULL       md:          \"f~b~\"       group:       \"Other\"     fe:       description: NULL       text:        NULL       md:          \"f~e~\"       group:       \"Other\"     half.life:       description: NULL       text:        NULL       md:          \"t~half~\"       group:       \"Common Parameters\"     kel.iv.last:       description: NULL       text:        NULL       md:          \"k~el,iv,last~\"       group:       \"Other\"     kel.iv.obs:       description: NULL       text:        NULL       md:          \"k~el,iv,obs~\"       group:       \"Other\"     kel.iv.pred:       description: NULL       text:        NULL       md:          \"k~el,iv,pred~\"       group:       \"Other\"     kel.last:       description: NULL       text:        NULL       md:          \"k~el,last~\"       group:       \"Other\"     kel.obs:       description: NULL       text:        NULL       md:          \"k~el,obs~\"       group:       \"Other\"     kel.pred:       description: NULL       text:        NULL       md:          \"k~el,pred~\"       group:       \"Other\"     lambda.z:       description: NULL       text:        \"λz\"       md:          \"<ff:symbol>l<\/ff>~z~\"       group:       \"Other\"     lambda.z.n.points:       description: NULL       text:        \"λz,n\"       md:          \"<ff:symbol>l<\/ff>~z,n~\"       group:       \"Other\"     lambda.z.time.first:       description: NULL       text:        \"λz (t 1ˢᵗ)\"       md:          \"<ff:symbol>l<\/ff>~z~ (t 1^st^)\"       group:       \"Other\"     mrt.iv.last:       description: NULL       text:        NULL       md:          \"MRT~iv,last~\"       group:       \"Other\"     mrt.iv.obs:       description: NULL       text:        NULL       md:          \"MRT~iv,obs~\"       group:       \"Other\"     mrt.iv.pred:       description: NULL       text:        NULL       md:          \"MRT~iv,pred~\"       group:       \"Other\"     mrt.last:       description: NULL       text:        NULL       md:          \"MRT~last~\"       group:       \"Other\"     mrt.md.obs:       description: NULL       text:        NULL       md:          \"MRT~md,obs~\"       group:       \"Other\"     mrt.md.pred:       description: NULL       text:        NULL       md:          \"MRT~md,pred~\"       group:       \"Other\"     mrt.obs:       description: NULL       text:        NULL       md:          \"MRT~obs~\"       group:       \"Other\"     mrt.pred:       description: NULL       text:        NULL       md:          \"MRT~pred~\"       group:       \"Other\"     ptr:       description: NULL       text:        NULL       md:          \"PTR\"       group:       \"Other\"     r.squared:       description: NULL       text:        \"r²\"       md:          \"R^2^\"       group:       \"Common Parameters\"     span.ratio:       description: NULL       text:        NULL       md:          \"SR\"       group:       \"Other\"     sparse_auclast:       description: NULL       text:        NULL       md:          \"AUC~last,sp~\"       group:       \"Common Parameters\"     start:       description: NULL       text:        NULL       md:          \"t~start~\"       group:       \"Other\"     swing:       description: NULL       text:        NULL       md:          \"SWING\"       group:       \"Other\"     tfirst:       description: NULL       text:        \"t 1ˢᵗ\"       md:          \"t~first~\"       group:       \"Other\"     thalf.eff.iv.last:       description: NULL       text:        NULL       md:          \"t~half,eff,iv,last~\"       group:       \"Other\"     thalf.eff.iv.obs:       description: NULL       text:        NULL       md:          \"t~half,eff,iv,obs~\"       group:       \"Other\"     thalf.eff.iv.pred:       description: NULL       text:        NULL       md:          \"t~half,eff,iv,pred~\"       group:       \"Other\"     thalf.eff.last:       description: NULL       text:        NULL       md:          \"t~half,eff,last~\"       group:       \"Other\"     thalf.eff.obs:       description: NULL       text:        NULL       md:          \"t~half,eff,obs~\"       group:       \"Other\"     thalf.eff.pred:       description: NULL       text:        NULL       md:          \"t~half,eff,pred~\"       group:       \"Other\"     time_above:       # JMH how is the conconcentration specified       description: NULL       text:        NULL       md:          \"t~ab~\"       group:       \"Other\"     tlag:       description: NULL       text:        NULL       md:          \"t~lag~\"       group:       \"Other\"     tlast:       description: NULL       text:        NULL       md:          \"t~last~\"       group:       \"Other\"     vd.obs:       description: NULL       text:        NULL       md:          \"V~d,obs~\"       group:       \"Other\"     vd.pred:       description: NULL       text:        NULL       md:          \"V~d,pred~\"       group:       \"Other\"     vss.iv.last:       description: NULL       text:        NULL       md:          \"V~ss,iv,last~\"       group:       \"Other\"     vss.iv.obs:       description: NULL       text:        NULL       md:          \"V~ss,iv,obs~\"       group:       \"Other\"     vss.iv.pred:       description: NULL       text:        NULL       md:          \"V~ss,iv,pred~\"       group:       \"Other\"     vss.last:       description: NULL       text:        NULL       md:          \"V~ss,iv,last~\"       group:       \"Other\"     vss.md.obs:       description: NULL       text:        NULL       md:          \"V~ss,md,obs~\"       group:       \"Other\"     vss.md.pred:       description: NULL       text:        NULL       md:          \"V~ss,md,pred~\"       group:       \"Other\"     vss.obs:       description: NULL       text:        NULL       md:          \"V~ss,obs~\"       group:       \"Other\"     vss.pred:       description: NULL       text:        NULL       md:          \"V~ss,pred~\"       group:       \"Other\"     vz.obs:       description: NULL       text:        NULL       md:          \"V~z,obs~\"       group:       \"Other\"     vz.pred:       description: NULL       text:        NULL       md:          \"V~z,pred~\"       group:       \"Other\"   units:     # For reference this is the units vignette from PKNCA     #     # http://billdenney.github.io/pknca/articles/v07-unit-conversion.html     #     # Default value for whether or not to include units. This can     # be overwritten in the app.     include_units: TRUE     # Add unit options here, they will appear in the order they are     # entered. The _def below is the default when a new analysis is     # initialize. It must be in the list.     # BILL how will PKNCA handle units like µg     conc:     [\"ng/mL\",   \"µg/mL\",  \"g/L\",    \"molar\",  \"nM\"]     conc_def:  \"ng/mL\"     dose:     [\"g\",  \"mg\",  \"mg/kg\",   \"µg\", \"µg/kg\",   \"ng\",   \"ng/kg\"]     dose_def:  \"mg\"     amt:      [\"g\",  \"mg\", \"µg\",  \"ng\", \"g\", \"moles\", \"µmoles\", \"nmoles\"]     amt_def:   \"mg\"     time:     [\"day\",     \"hr\",      \"min\",    \"sec\"         ]     time_def:  \"day\"     vol:      [\"mL\",     \"L\"]   # This is used to generate documentation about required columns in datasets.   # Yous hould only change the Column and Description elements. The others are   # used internally.   data_format:     columns:      - entry:         sname:       \"id\"         Column:      \"Subject ID\"         Required:    \"yes\"         Description: \"Unique subject identifier.\"      - entry:         sname:       \"time\"         Column:      \"Time\"         Required:    \"yes\"         Description: \"Time since the first dose. Must be numeric.\"      - entry:         sname:       \"ntime\"         Column:      \"Nominal Time\"         Required:    \"yes\"         Description: \"Time since the last dose in the cycle (same units as Time). Must be numeric.\"      - entry:         sname:       \"conc\"         Column:      \"Concentration\"         Required:    \"yes\"         Description: \"Observed concentration. Must be numeric, no NA values. Missing values should be set to 0.\"      # BILL should this be the dose given at time zero or total dose given over a cycle      - entry:         sname:       \"dose\"         Column:      \"Dose\"         Required:    \"yes\"         Description: \"Value of dose given (must be numeric).\"      - entry:         sname:       \"route\"         Column:      \"Route\"         Required:    \"yes\"         Description: \"Doseing route. It should be either 'extravascular' or 'intravascular' but ruminate will make an effort to figure out other values.\"      - entry:         sname:       \"dur\"         Column:      \"Duration (optional)\"         Required:    \"no\"         Description: \"Doseing duration (same units as Time). If set to N/A a value of 0 will be assumed.\"      - entry:         sname:       \"cycle\"         Column:      \"Dose cycle\"         Required:    \"yes\"         Description: \"Current dose cycle\""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"creating-subjects","dir":"Articles > Rmdhunks","previous_headings":"","what":"Creating subjects","title":"","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"covariates","dir":"Articles > Rmdhunks","previous_headings":"Creating subjects","what":"Covariates","title":"","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/articles/rmdhunks/simulate_rules.html","id":"rule-based-simulations","dir":"Articles > Rmdhunks","previous_headings":"","what":"Rule-based simulations","title":"","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"rules","dir":"Articles > Rmdhunks","previous_headings":"Rule-based simulations","what":"Rules","title":"","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"rule-evaluation-environment","dir":"Articles > Rmdhunks","previous_headings":"Rule-based simulations","what":"Rule-evaluation environment","title":"","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/articles/rmdhunks/simulate_rules.html","id":"the-following-functions-will-be-available","dir":"Articles > Rmdhunks","previous_headings":"Rule-based simulations > Rule-evaluation environment","what":"The following functions will be available:","title":"","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"John Harrold. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Harrold J (2024). ruminate: Pharmacometrics Data Transformation Analysis Tool. R package version 0.2.3, https://ruminate.ubiquity.tools/.","code":"@Manual{,   title = {ruminate: A Pharmacometrics Data Transformation and Analysis Tool},   author = {John Harrold},   year = {2024},   note = {R package version 0.2.3},   url = {https://ruminate.ubiquity.tools/}, }"},{"path":"/index.html","id":"ruminate-","dir":"","previous_headings":"","what":"A Pharmacometrics Data Transformation and Analysis Tool","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"goal ruminate facilitate exploration pharmacometrics data. done creating Shiny interface different tools data transformation (dplyr tidyr), plotting (ggplot2), noncompartmental analysis (PKNCA). results can reported Excel, Word PowerPoint. state app can saved loaded later date. saved, script generated reproduce different actions Shiny interface.","code":""},{"path":"/index.html","id":"deployed-example-in-the-cloud","dir":"","previous_headings":"","what":"Deployed example in the cloud","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"can point web browser runrumiate.ubiquity.tools try development version ruminate. Keep mind public server. data tracked kept, open public. careful uploading confidential data. Also know single instance many people using can slow.","code":""},{"path":[]},{"path":"/index.html","id":"from-cran","dir":"","previous_headings":"","what":"From CRAN","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"can install released version rumiante CRAN :","code":"# General dependencies: install.packages(\"clipr\") install.packages(\"gridExtra\") install.packages(\"prompter\") install.packages(\"readxl\") install.packages(\"shinydashboard\") install.packages(\"ubiquity\")  # Dependencies for the MB and CTS modules install.packages(\"nlmixr2lib\") install.packages(\"nonmem2rx\") install.packages(\"rxode2\")  # Actual package install.packages(\"ruminate\")"},{"path":"/index.html","id":"development-version","dir":"","previous_headings":"","what":"Development version","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"can install development version GitHub following: Note ruminate depends formods onbrand need first install development versions onbrand formods.","code":"# Installing devtools if it's not already installed if(system.file(package=\"devtools\") == \"\"){   install.packages(\"devtools\")  }  # General dependencies: install.packages(\"clipr\") install.packages(\"gridExtra\") install.packages(\"prompter\") install.packages(\"readxl\") install.packages(\"shinydashboard\") install.packages(\"ubiquity\")  # Dependencies for the MB and CTS modules install.packages(\"nlmixr2lib\") install.packages(\"nonmem2rx\") install.packages(\"rxode2\")  devtools::install_github(\"john-harrold/onbrand\",  dependencies=TRUE) devtools::install_github(\"john-harrold/formods\",  dependencies=TRUE) devtools::install_github(\"john-harrold/ruminate\")"},{"path":[]},{"path":"/index.html","id":"in-rstudio","dir":"","previous_headings":"","what":"In RStudio","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"","code":"library(ruminate) ruminate()"},{"path":"/index.html","id":"in-a-docker-container","dir":"","previous_headings":"","what":"In a Docker container","title":"A Pharmacometrics Data Transformation and Analysis Tool","text":"Note uses x86 image. work Intel AMD cpus. means wont get far M1 mac. want, can download Docker file . build docker image repository GitHub. run Docker image locally: everything worked can point web browser : push image DockerHub: can use pull image DockerHub:","code":"docker build -t ruminate --no-cache  https://raw.githubusercontent.com/john-harrold/ruminate/main/inst/docker/local_container docker run --name ruminate --rm -p 3838:3838 ruminate http://127.0.0.1:3838/ docker tag ruminate <username>/ruminate:latest docker push <username>/ruminate:latest docker pull <username>/ruminate:latest"},{"path":"/reference/CTS_Server.html","id":null,"dir":"Reference","previous_headings":"","what":"Clinical Trial Simulator State Server — CTS_Server","title":"Clinical Trial Simulator State Server — CTS_Server","text":"Server function Clinical Trial Simulator  Shiny Module","code":""},{"path":"/reference/CTS_Server.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clinical Trial Simulator State Server — CTS_Server","text":"","code":"CTS_Server(   id,   id_ASM = \"ASM\",   id_MB = \"MB\",   FM_yaml_file = system.file(package = \"formods\", \"templates\", \"formods.yaml\"),   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"CTS.yaml\"),   deployed = FALSE,   react_state = NULL )"},{"path":"/reference/CTS_Server.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clinical Trial Simulator State Server — CTS_Server","text":"id ID string corresponds ID used call modules UI elements id_ASM ID string app state managment module used save load app states id_MB ID string corresponds ID used call MB modules FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. deployed Boolean variable indicating whether app deployed . react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/CTS_Server.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clinical Trial Simulator State Server — CTS_Server","text":"UD Server object","code":""},{"path":"/reference/CTS_add_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Covariate to Elemetnt — CTS_add_covariate","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"Takes ui elements module state processes covariate elements addition.","code":""},{"path":"/reference/CTS_add_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"","code":"CTS_add_covariate(state, element)"},{"path":"/reference/CTS_add_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_add_covariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"Element results adding covariate. cares list element can used determine exit status function. COV_IS_GOOD  TRUE covariate good added, FALSE issues. msgs     Vector messages.","code":""},{"path":"/reference/CTS_add_covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"depends following UI values state state[[\"CTS\"]][[\"ui\"]][[\"covariate_value\"]] state[[\"CTS\"]][[\"ui\"]][[\"covariate_type_selected\"]] state[[\"CTS\"]][[\"ui\"]][[\"selected_covariate\"]]","code":""},{"path":"/reference/CTS_add_covariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Covariate to Elemetnt — CTS_add_covariate","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) #> Warning: package ‘formods’ was built under R version 4.2.3 #> Loading formods if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: a6517a625b3252139391dc2e9f59f50a #> → MB: module checksum updated:3149cb1e017d6388b2898f3c772486b4 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: a890472739a3bfdcf1611d936eeba15b #> → CTS: module checksum updated: 53e1afc18174e7a020ff9f75bb34c963 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: fa03ed2bad3415184a53a5acfff96b8a #> → CTS: module checksum updated: 15c571c86d03c87893193a8e68035492"},{"path":"/reference/CTS_add_rule.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Rule to Element — CTS_add_rule","title":"Add Rule to Element — CTS_add_rule","text":"Takes ui elements state element attempts add rule.","code":""},{"path":"/reference/CTS_add_rule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Rule to Element — CTS_add_rule","text":"","code":"CTS_add_rule(state, element)"},{"path":"/reference/CTS_add_rule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Rule to Element — CTS_add_rule","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_add_rule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Rule to Element — CTS_add_rule","text":"Element results adding rule. rares list element can used determine exit status function. RULE_IS_GOOD  true indicates pieces rule UI check . RULE_UPDATED  RULE_IS_GOOD RULE_UPDATED true previous rule definition overwritten. RULE_IS_GOOD TRUE RULE_UPDATED FALSE new rule added. notify_text  Text notify message notify_id    Notification ID notify_type  Notification type msgs         Vector messages.","code":""},{"path":"/reference/CTS_add_rule.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Rule to Element — CTS_add_rule","text":"depends following UI values state element state[\"CTS\"][\"ui\"][\"rule_name\"] state[\"CTS\"][\"ui\"][\"rule_condition\"] state[\"CTS\"][\"ui\"][\"rule_type\"] rule type \"dose\" state[\"CTS\"][\"ui\"][\"action_dosing_state\"] state[\"CTS\"][\"ui\"][\"action_dosing_values\"] state[\"CTS\"][\"ui\"][\"action_dosing_times\"] state[\"CTS\"][\"ui\"][\"action_dosing_durations\"] rule type \"set state\" state[\"CTS\"][\"ui\"][\"action_set_state_state\"] state[\"CTS\"][\"ui\"][\"action_set_state_values\"] rule type \"manual code\" state[\"CTS\"][\"ui\"][\"action_manual_code\"]","code":""},{"path":"/reference/CTS_add_rule.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Rule to Element — CTS_add_rule","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 08583be5e6d971169e6ab1366416c505 #> → CTS: module checksum updated: e79d2623868730282770f9284cdf03dd #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: cd4c562a774ff3fbe46a0c34d478c05e #> → CTS: module checksum updated: 44875efaa01517e967afab541affccc9"},{"path":"/reference/CTS_append_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Report Elements — CTS_append_report","title":"Append Report Elements — CTS_append_report","text":"Appends report elements formods report.","code":""},{"path":"/reference/CTS_append_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Report Elements — CTS_append_report","text":"","code":"CTS_append_report(state, rpt, rpttype, gen_code_only = FALSE)"},{"path":"/reference/CTS_append_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Report Elements — CTS_append_report","text":"state CTS state CTS_fetch_state() rpt Report current content report appended function. details structure see documentation template_details rpttype Type report generate (supported \"xlsx\", \"pptx\", \"docx\"). gen_code_only Boolean value indicating code generated (FALSE).","code":""},{"path":"/reference/CTS_append_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Report Elements — CTS_append_report","text":"list containing following elements isgood:    Return status function. hasrptele: Boolean indicator module reportable elements. code:      Code generate reporting elements. msgs:      Messages passed back user. rpt:       Report additions passed back user.","code":""},{"path":[]},{"path":"/reference/CTS_change_source_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Change the Source Model — CTS_change_source_model","title":"Change the Source Model — CTS_change_source_model","text":"Takes ui elements state element processes changes source model updates element accordingly.","code":""},{"path":"/reference/CTS_change_source_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change the Source Model — CTS_change_source_model","text":"","code":"CTS_change_source_model(state, element)"},{"path":"/reference/CTS_change_source_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change the Source Model — CTS_change_source_model","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_change_source_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change the Source Model — CTS_change_source_model","text":"Element necessary changes source model.","code":""},{"path":"/reference/CTS_change_source_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Change the Source Model — CTS_change_source_model","text":"depends following UI values state. state[\"CTS\"][\"ui\"][\"source_model\"]","code":""},{"path":"/reference/CTS_change_source_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change the Source Model — CTS_change_source_model","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 935881096cbd16e6975d650488038bbf #> → CTS: module checksum updated: f44ed9bf2c959f9a04068b814e4f068f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: c80ec5c0395e9ecc4faa148bb59760ea #> → CTS: module checksum updated: 6f8380ea8efd3e6983d5f68af8741c26"},{"path":"/reference/CTS_del_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Deletes Current cohort — CTS_del_current_element","title":"Deletes Current cohort — CTS_del_current_element","text":"Takes CTS state deletes current cohort. last element, new default added.","code":""},{"path":"/reference/CTS_del_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deletes Current cohort — CTS_del_current_element","text":"","code":"CTS_del_current_element(state)"},{"path":"/reference/CTS_del_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deletes Current cohort — CTS_del_current_element","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_del_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deletes Current cohort — CTS_del_current_element","text":"CTS state object current cohort deleted.","code":""},{"path":"/reference/CTS_del_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deletes Current cohort — CTS_del_current_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 9322acda5a3f497a6a34a643af97572d #> → CTS: module checksum updated: cc2d97ebe541372ee34e9c1156331c50 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 1f97f308e2c855147b2c07f565f83970 #> → CTS: module checksum updated: e4a7db4c31e85accf7c88c16eaf9bb86"},{"path":"/reference/CTS_fetch_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Code — CTS_fetch_code","title":"Fetch Module Code — CTS_fetch_code","text":"Fetches code generate results seen app","code":""},{"path":"/reference/CTS_fetch_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Code — CTS_fetch_code","text":"","code":"CTS_fetch_code(state)"},{"path":"/reference/CTS_fetch_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Code — CTS_fetch_code","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Code — CTS_fetch_code","text":"Character object vector lines code","code":""},{"path":"/reference/CTS_fetch_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Code — CTS_fetch_code","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 59c707ab86c6228f7ff33481cd21655a #> → CTS: module checksum updated: 88ad6e89f4b6392b2ef8eacdd2598f0b #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 0a5804e808d8bce5186653bfe1d2bced #> → CTS: module checksum updated: 8d0060c6f0d82de4c3d99ec0b44d99e7"},{"path":"/reference/CTS_fetch_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Current cohort — CTS_fetch_current_element","title":"Fetches Current cohort — CTS_fetch_current_element","text":"Takes CTS state returns current active cohort","code":""},{"path":"/reference/CTS_fetch_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Current cohort — CTS_fetch_current_element","text":"","code":"CTS_fetch_current_element(state)"},{"path":"/reference/CTS_fetch_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Current cohort — CTS_fetch_current_element","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Current cohort — CTS_fetch_current_element","text":"List containing details active data view. structure list structure state$CTS$elements output CTS_fetch_state().","code":""},{"path":"/reference/CTS_fetch_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Current cohort — CTS_fetch_current_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 76a18c3abc6e9f1f4708db64e30e2e31 #> → CTS: module checksum updated: 9f33b1700fde5dd80ea4badc2569a61c #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 531532f571eebea1826ccc63c17fb98a #> → CTS: module checksum updated: b68fababd66ddda98db4403f7e8b1c70"},{"path":"/reference/CTS_fetch_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"Fetches datasets contained module.","code":""},{"path":"/reference/CTS_fetch_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"","code":"CTS_fetch_ds(state)"},{"path":"/reference/CTS_fetch_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"Character object vector lines code list containing following elements isgood:    Return status function. hasds:     Boolean indicator module datasets msgs:      Messages passed back user. ds:        List datasets. list element name R-object dataset. element following structure: label: Text label dataset MOD_TYPE: Short name type module. id: module ID DS: Dataframe containing actual dataset. DSMETA: Metadata describing DS code: Complete code build dataset. checksum: Module checksum. DSchecksum: Dataset checksum.","code":""},{"path":"/reference/CTS_fetch_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Clinical Trial Simulator Module Datasets — CTS_fetch_ds","text":"","code":"# We need a module state: sess_res = CTS_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: f5a0a431e4e9e627930edebefd192455 #> → CTS: module checksum updated: aeab9fffaba1ec1cd917ec30144b0e90 #> → CTS: source model change detected #> → CTS:  > covariates reset #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: 1b4ac211aa2c5b86e6c80fe5ee0086aa #> → CTS: module checksum updated: 206b2fb29f12642234b1952d9cd188f2 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #> → CTS: cohort checksum updated: c2deb5a6d2303193adec2ddfa561f53d #> → CTS: module checksum updated: 165af86bb00da1f8cf3e6a84ec76d792 state = sess_res$state  ds = CTS_fetch_ds(state)  ds #> $hasds #> [1] FALSE #>  #> $isgood #> [1] TRUE #>  #> $msgs #> NULL #>  #> $ds #> list() #>"},{"path":"/reference/CTS_fetch_mdl.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"Fetches models contained module.","code":""},{"path":"/reference/CTS_fetch_mdl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"","code":"CTS_fetch_mdl(state)"},{"path":"/reference/CTS_fetch_mdl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_fetch_mdl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"list containing following elements isgood:    Return status function. hasmdl:    Boolean indicator module models msgs:      Messages passed back user. mdl:       List models. list element name R-object dataset. element following structure: label:      Text label model (e.g. one-compartment model). MOD_TYPE:   Type module. id:         Module ID. rx_obj:     rxode2 object name holds model. fcn_def:    Text define model MDLMETA:    Notes model. code:       Code generate model. checksum:   Module checksum. MDLchecksum: Model checksum.","code":""},{"path":"/reference/CTS_fetch_mdl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Clinical Trial Simulator Module Models — CTS_fetch_mdl","text":"","code":"# We need a module state: sess_res = CTS_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> Error in has_updated(old_chk, new_chk): could not find function \"has_updated\" state = sess_res$state #> Error in eval(expr, envir, enclos): object 'sess_res' not found  mdls = CTS_fetch_mdl(state) #> Error in CTS_fetch_mdl(state): object 'state' not found  names(mdls) #> Error in eval(expr, envir, enclos): object 'mdls' not found"},{"path":"/reference/CTS_fetch_sc_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"provides meta information simulatino options. includes option names, text descriptions, ui_names used, etc.","code":""},{"path":"/reference/CTS_fetch_sc_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"","code":"CTS_fetch_sc_meta(   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"CTS.yaml\") )"},{"path":"/reference/CTS_fetch_sc_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"MOD_yaml_file Module configuration file MC main section.","code":""},{"path":"/reference/CTS_fetch_sc_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"List following elements: config List YAML->MC->sim_config. summary: Dataframe elements config tabular format. ui_config Vector  ui_ids configuration options.","code":""},{"path":"/reference/CTS_fetch_sc_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Simulation Parameter Meta Information — CTS_fetch_sc_meta","text":"","code":"CTS_fetch_sc_meta() #> $config #> $config$atol #> $config$atol$group #> [1] \"ODE Solver\" #>  #> $config$atol$label #> [1] \"Absolute tolerance\" #>  #> $config$atol$tooltip #> [1] \"Absolute tolerance used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $config$atol$uitype #> [1] \"text\" #>  #> $config$atol$type #> [1] \"numeric\" #>  #> $config$atol$value #> [1] \"1e-8\" #>  #> $config$atol$options #> NULL #>  #> $config$atol$use #> [1] \"rxSolve\" #>  #> $config$atol$placeholder #> [1] \"1e-8\" #>  #> $config$atol$ui #> [1] \"cts_config_atol\" #>  #>  #> $config$rtol #> $config$rtol$group #> [1] \"ODE Solver\" #>  #> $config$rtol$label #> [1] \"Relative tolerance\" #>  #> $config$rtol$tooltip #> [1] \"Relative tolerance used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $config$rtol$uitype #> [1] \"text\" #>  #> $config$rtol$type #> [1] \"numeric\" #>  #> $config$rtol$value #> [1] \"1e-6\" #>  #> $config$rtol$options #> NULL #>  #> $config$rtol$use #> [1] \"rxSolve\" #>  #> $config$rtol$placeholder #> [1] \"1e-6\" #>  #> $config$rtol$ui #> [1] \"cts_config_rtol\" #>  #>  #> $config$sigdig #> $config$sigdig$group #> [1] \"ODE Solver\" #>  #> $config$sigdig$label #> [1] \"Significant digits\" #>  #> $config$sigdig$tooltip #> [1] \"Specifies the \\\"significant digits\\\" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\\\\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\\\\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\\\\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.\" #>  #> $config$sigdig$uitype #> [1] \"text\" #>  #> $config$sigdig$type #> [1] \"numeric\" #>  #> $config$sigdig$value #> [1] \"NULL\" #>  #> $config$sigdig$options #> NULL #>  #> $config$sigdig$use #> [1] \"rxSolve\" #>  #> $config$sigdig$placeholder #> [1] \"NULL\" #>  #> $config$sigdig$ui #> [1] \"cts_config_sigdig\" #>  #>  #> $config$seed #> $config$seed$group #> [1] \"Misc\" #>  #> $config$seed$label #> [1] \"Random Seed\" #>  #> $config$seed$tooltip #> [1] \"Integer value to set the seed for the random number generator\" #>  #> $config$seed$uitype #> [1] \"text\" #>  #> $config$seed$type #> [1] \"numeric\" #>  #> $config$seed$value #> [1] \"86753090\" #>  #> $config$seed$options #> NULL #>  #> $config$seed$use #> [1] \"Misc\" #>  #> $config$seed$placeholder #> [1] \"8675309\" #>  #> $config$seed$ui #> [1] \"cts_config_seed\" #>  #>  #> $config$nsteps #> $config$nsteps$group #> [1] \"Misc\" #>  #> $config$nsteps$label #> [1] \"Time Steps\" #>  #> $config$nsteps$tooltip #> [1] \"Integer value representing the number of time steps to add between zero and the end of trial. A larger value will give smoother curves but it will also take more time to simulate.\" #>  #> $config$nsteps$uitype #> [1] \"text\" #>  #> $config$nsteps$type #> [1] \"numeric\" #>  #> $config$nsteps$value #> [1] \"200\" #>  #> $config$nsteps$options #> NULL #>  #> $config$nsteps$use #> [1] \"Misc\" #>  #> $config$nsteps$placeholder #> [1] \"200\" #>  #> $config$nsteps$ui #> [1] \"cts_config_nsteps\" #>  #>  #> $config$preamble #> $config$preamble$group #> [1] \"Rule Environment Preamble\" #>  #> $config$preamble$label #> NULL #>  #> $config$preamble$tooltip #> [1] \"Optional user-defined code to be executed in the rule-evaluation environment. You can create user-defined functions here.\" #>  #> $config$preamble$uitype #> [1] \"textarea\" #>  #> $config$preamble$type #> [1] \"character\" #>  #> $config$preamble$value #> NULL #>  #> $config$preamble$options #> NULL #>  #> $config$preamble$use #> [1] \"rules\" #>  #> $config$preamble$placeholder #> [1] \"my_func = function(id=NULL){\\n\\n}\" #>  #> $config$preamble$ui #> [1] \"cts_config_preamble\" #>  #>  #>  #> $cfg_summary #>       name                  ui     use      type                     group #> 1     atol     cts_config_atol rxSolve   numeric                ODE Solver #> 2     rtol     cts_config_rtol rxSolve   numeric                ODE Solver #> 3   sigdig   cts_config_sigdig rxSolve   numeric                ODE Solver #> 4     seed     cts_config_seed    Misc   numeric                      Misc #> 5   nsteps   cts_config_nsteps    Misc   numeric                      Misc #> 6 preamble cts_config_preamble   rules character Rule Environment Preamble #>  #> $ui_config #> [1] \"cts_config_atol\"     \"cts_config_rtol\"     \"cts_config_sigdig\"   #> [4] \"cts_config_seed\"     \"cts_config_nsteps\"   \"cts_config_preamble\" #>"},{"path":"/reference/CTS_fetch_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Clinical Trial Simulator State — CTS_fetch_state","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"Merges default app options changes made UI","code":""},{"path":"/reference/CTS_fetch_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"","code":"CTS_fetch_state(   id,   id_ASM,   id_MB,   input,   session,   FM_yaml_file,   MOD_yaml_file,   react_state )"},{"path":"/reference/CTS_fetch_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"id Shiny module ID id_ASM ID string app state management module used save load app states id_MB ID string corresponds ID used call MB modules input Shiny input variable session Shiny session variable FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/CTS_fetch_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. CTS: isgood: Boolean object indicating file successfully loaded. checksum: MD5 sum contents element can used detect changes state. MOD_TYPE: Character data containing type module \"CTS\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/CTS_fetch_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Clinical Trial Simulator State — CTS_fetch_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = CTS_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 360f8b9307c486e662836148e1f02e0c #> → CTS: module checksum updated: 194c5a9d38b07c3f460584172c991b84 #> → CTS: source model change detected #> → CTS:  > covariates reset #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: 1b4ac211aa2c5b86e6c80fe5ee0086aa #> → CTS: module checksum updated: c243ca52aba9ad8a3bb2228dedcd0b67 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #> → CTS: cohort checksum updated: f02d4b20743252d7d21abfa8412c0dc5 #> → CTS: module checksum updated: f51e52188412a77c702aaee3432b40ef session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"CTS.yaml\")  # Creating an empty state object state = CTS_fetch_state(id              = \"CTS\",                        id_ASM          = \"ASM\",                        id_MB           = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)"},{"path":"/reference/CTS_init_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize CTS Module State — CTS_init_state","title":"Initialize CTS Module State — CTS_init_state","text":"Creates list initialized module state","code":""},{"path":"/reference/CTS_init_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize CTS Module State — CTS_init_state","text":"","code":"CTS_init_state(FM_yaml_file, MOD_yaml_file, id, id_MB, session)"},{"path":"/reference/CTS_init_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize CTS Module State — CTS_init_state","text":"FM_yaml_file App configuration file FM main section MOD_yaml_file Module configuration file MC main section id ID string module id_MB ID string corresponds ID used call MB modules session Shiny session variable","code":""},{"path":"/reference/CTS_init_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize CTS Module State — CTS_init_state","text":"list containing empty CTS state","code":""},{"path":"/reference/CTS_init_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize CTS Module State — CTS_init_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = CTS_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: cc14b137e9e94bb79d7bc711f5bc8517 #> → CTS: module checksum updated: 76cc0ad8bbc4ebb0a4ded24b0a3afd09 #> → CTS: source model change detected #> → CTS:  > covariates reset #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: 1b4ac211aa2c5b86e6c80fe5ee0086aa #> → CTS: module checksum updated: 1abdae1e84ac314befa02951b742bee4 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #>   #>   #> Warning: multi-subject simulation without without 'omega' #> Warning: IDs without zero-time start at the first observed time: 10 9 8 7 6 5 4 3 2 1 #> → CTS: cohort checksum updated: 37a6484c228e41e5096bd6a6ad9b8994 #> → CTS: module checksum updated: 166cd3d4d74267506ed1235972ff038b session = sess_res$session input   = sess_res$input  state = CTS_init_state(    FM_yaml_file  = system.file(package = \"formods\",                                \"templates\",                                \"formods.yaml\"),    MOD_yaml_file = system.file(package = \"ruminate\",                                \"templates\",                                \"CTS.yaml\"),    id              = \"CTS\",    id_MB           = \"MB\",    session         = session) #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized  state #> $yaml #> $yaml$FM #> $yaml$FM$include #> $yaml$FM$include$files #> $yaml$FM$include$files[[1]] #> $yaml$FM$include$files[[1]]$file #> $yaml$FM$include$files[[1]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.docx\\\")\" #>  #> $yaml$FM$include$files[[1]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.docx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[2]] #> $yaml$FM$include$files[[2]]$file #> $yaml$FM$include$files[[2]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.pptx\\\")\" #>  #> $yaml$FM$include$files[[2]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.pptx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[3]] #> $yaml$FM$include$files[[3]]$file #> $yaml$FM$include$files[[3]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.yaml\\\")\" #>  #> $yaml$FM$include$files[[3]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.yaml\\\")\" #>  #>  #>  #>  #>  #> $yaml$FM$deployed #> [1] FALSE #>  #> $yaml$FM$code #> $yaml$FM$code$theme #> [1] \"vibrant_ink\" #>  #> $yaml$FM$code$showLineNumbers #> [1] TRUE #>  #> $yaml$FM$code$gen_file #> [1] \"run_analysis.R\" #>  #> $yaml$FM$code$gen_preamble #> [1] \"# formods automated output ------------------------------------------------\\n# https://formods.ubiquity.tools/\\nrm(list=ls())\" #>  #> $yaml$FM$code$packages #> [1] \"onbrand\" \"writexl\" #>  #>  #> $yaml$FM$notifications #> $yaml$FM$notifications$config #> $yaml$FM$notifications$config$success #> $yaml$FM$notifications$config$success$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$background #> [1] \"#5bb85b\" #>  #>  #> $yaml$FM$notifications$config$failure #> $yaml$FM$notifications$config$failure$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$background #> [1] \"#d9534f\" #>  #>  #> $yaml$FM$notifications$config$info #> $yaml$FM$notifications$config$info$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$background #> [1] \"#5bc0de\" #>  #>  #> $yaml$FM$notifications$config$warning #> $yaml$FM$notifications$config$warning$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$background #> [1] \"#f0ac4d\" #>  #>  #>  #>  #> $yaml$FM$reporting #> $yaml$FM$reporting$enabled #> [1] TRUE #>  #> $yaml$FM$reporting$content_init #> $yaml$FM$reporting$content_init$xlsx #> [1] \"rpt = list(summary = NULL,\\n           sheets  = list())\" #>  #> $yaml$FM$reporting$content_init$docx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.docx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #> $yaml$FM$reporting$content_init$pptx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.pptx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #>  #>  #> $yaml$FM$ui #> $yaml$FM$ui$button_style #> [1] \"fill\" #>  #> $yaml$FM$ui$select_size #> [1] 10 #>  #> $yaml$FM$ui$color_green #> [1] \"#00BB8A\" #>  #> $yaml$FM$ui$color_red #> [1] \"#FF475E\" #>  #> $yaml$FM$ui$color_blue #> [1] \"#0088FF\" #>  #> $yaml$FM$ui$color_purple #> [1] \"#bd2cf4\" #>  #>  #> $yaml$FM$data_meta #> $yaml$FM$data_meta$data_header #> [1] \"<span style='color:===COLOR==='><b>===NAME===<\/b><br/><font size='-3'>===LABEL===<\/font><\/span>\" #>  #> $yaml$FM$data_meta$subtext #> [1] \"===LABEL===: ===RANGE===\" #>  #> $yaml$FM$data_meta$many_sep #> [1] \",⋅⋅⋅,\" #>  #> $yaml$FM$data_meta$data_types #> $yaml$FM$data_meta$data_types$character #> $yaml$FM$data_meta$data_types$character$color #> [1] \"#DD4B39\" #>  #> $yaml$FM$data_meta$data_types$character$label #> [1] \"text\" #>  #>  #> $yaml$FM$data_meta$data_types$double #> $yaml$FM$data_meta$data_types$double$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$double$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$integer #> $yaml$FM$data_meta$data_types$integer$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$integer$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$other #> $yaml$FM$data_meta$data_types$other$color #> [1] \"black\" #>  #> $yaml$FM$data_meta$data_types$other$label #> [1] \"other\" #>  #>  #>  #>  #> $yaml$FM$labels #> $yaml$FM$labels$default_ds #> [1] \"Original data set\" #>  #>  #> $yaml$FM$user_files #> $yaml$FM$user_files$use_tmpdir #> [1] TRUE #>  #>  #> $yaml$FM$logging #> $yaml$FM$logging$enabled #> [1] TRUE #>  #> $yaml$FM$logging$timestamp #> [1] TRUE #>  #> $yaml$FM$logging$timestamp_fmt #> [1] \"%Y-%m-%d %H:%M:%S\" #>  #> $yaml$FM$logging$log_file #> [1] \"formods_log.txt\" #>  #> $yaml$FM$logging$console #> [1] TRUE #>  #>  #>  #>  #> $MC #> $MC$element_object_name #> [1] \"CTS_obj\" #>  #> $MC$code #> $MC$code$packages #> [1] \"rxode2\"     \"nonmem2rx\"  \"nlmixr2lib\" #>  #> $MC$code$readOnly #> [1] TRUE #>  #> $MC$code$mode #> [1] \"r\" #>  #>  #> $MC$compact #> $MC$compact$code #> [1] TRUE #>  #> $MC$compact$clip #> [1] TRUE #>  #>  #> $MC$reporting #> $MC$reporting$enabled #> [1] FALSE #>  #> $MC$reporting$priority #> [1] 1 #>  #>  #> $MC$formatting #> $MC$formatting$code #> $MC$formatting$code$width #> [1] 800 #>  #> $MC$formatting$code$height #> [1] 300 #>  #>  #> $MC$formatting$current_element #> $MC$formatting$current_element$width #> [1] \"200px\" #>  #>  #> $MC$formatting$source_model #> $MC$formatting$source_model$width #> [1] \"200px\" #>  #>  #> $MC$formatting$element_name #> $MC$formatting$element_name$width #> [1] \"200px\" #>  #>  #> $MC$formatting$preview #> $MC$formatting$preview$width #> [1] \"800px\" #>  #> $MC$formatting$preview$height #> [1] \"500px\" #>  #>  #> $MC$formatting$input #> NULL #>  #> $MC$formatting$button_clk_run #> $MC$formatting$button_clk_run$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_run$block #> [1] TRUE #>  #>  #> $MC$formatting$button_clk_del #> $MC$formatting$button_clk_del$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_del$block #> [1] TRUE #>  #> $MC$formatting$button_clk_del$tooltip #> [1] \"Delete the current cohort.\" #>  #> $MC$formatting$button_clk_del$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_save #> $MC$formatting$button_clk_save$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_save$block #> [1] TRUE #>  #> $MC$formatting$button_clk_save$tooltip #> [1] \"Save the name for the current cohort.\" #>  #> $MC$formatting$button_clk_save$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_runsim #> $MC$formatting$button_clk_runsim$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_runsim$block #> [1] TRUE #>  #> $MC$formatting$button_clk_runsim$tooltip #> [1] \"Simulate or resimulate the cohort with the current rules applied.\" #>  #> $MC$formatting$button_clk_runsim$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_update_plot #> $MC$formatting$button_clk_update_plot$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_update_plot$block #> [1] TRUE #>  #> $MC$formatting$button_clk_update_plot$tooltip #> [1] \"Update timecourse figures.\" #>  #> $MC$formatting$button_clk_update_plot$tooltip_position #> [1] \"top\" #>  #>  #> $MC$formatting$button_clk_clip #> $MC$formatting$button_clk_clip$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_clip$block #> [1] TRUE #>  #> $MC$formatting$button_clk_clip$tooltip #> [1] \"Copy the code to generate the current cohort to the clipboard.\" #>  #> $MC$formatting$button_clk_clip$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_copy #> $MC$formatting$button_clk_copy$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_copy$block #> [1] TRUE #>  #> $MC$formatting$button_clk_copy$tooltip #> [1] \"Make a copy of the current cohort.\" #>  #> $MC$formatting$button_clk_copy$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_new #> $MC$formatting$button_clk_new$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_new$block #> [1] TRUE #>  #> $MC$formatting$button_clk_new$tooltip #> [1] \"Create a new cohort.\" #>  #> $MC$formatting$button_clk_new$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_add_rule #> $MC$formatting$button_clk_add_rule$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_add_rule$block #> [1] TRUE #>  #> $MC$formatting$button_clk_add_rule$tooltip #> [1] \"Add a new rule.\" #>  #> $MC$formatting$button_clk_add_rule$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_add_cov #> $MC$formatting$button_clk_add_cov$width #> [1] \"456px\" #>  #> $MC$formatting$button_clk_add_cov$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_add_cov$block #> [1] TRUE #>  #> $MC$formatting$button_clk_add_cov$tooltip #> [1] \"Define covariate.\" #>  #> $MC$formatting$button_clk_add_cov$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$rule_condition #> $MC$formatting$rule_condition$width #> [1] 300 #>  #> $MC$formatting$rule_condition$height #> [1] 100 #>  #> $MC$formatting$rule_condition$tooltip #> [1] \"This is a condition that should evaluate to either TRUE (if the action should be taken) or FALSE (if it should be skipped).\" #>  #> $MC$formatting$rule_condition$tooltip_position #> [1] \"bottom\" #>  #> $MC$formatting$rule_condition$placeholder #> [1] \"examples: TRUE will run the action at at every visit or (time > 0) & (Ac < 10) will be more selective.\" #>  #>  #> $MC$formatting$action_dosing_state #> $MC$formatting$action_dosing_state$width #> [1] 400 #>  #>  #> $MC$formatting$action_dosing_values #> $MC$formatting$action_dosing_values$width #> [1] 400 #>  #> $MC$formatting$action_dosing_values$placeholder #> [1] \"c(1, 1, 1)\" #>  #> $MC$formatting$action_dosing_values$tooltip #> [1] \"Expression defining dosing amounts that will evaluate as a numeric vector of numbers.\" #>  #>  #> $MC$formatting$action_dosing_times #> $MC$formatting$action_dosing_times$width #> [1] 400 #>  #> $MC$formatting$action_dosing_times$placeholder #> [1] \"c(0, 7, 14)\" #>  #> $MC$formatting$action_dosing_times$tooltip #> [1] \"Expression defining dosing times that will evaluate as a numeric vector of numbers.\" #>  #> $MC$formatting$action_dosing_times$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$action_dosing_durations #> $MC$formatting$action_dosing_durations$width #> [1] 400 #>  #> $MC$formatting$action_dosing_durations$placeholder #> [1] \"c(0, 0, 0)\" #>  #> $MC$formatting$action_dosing_durations$tooltip #> [1] \"Expression containing dosing duration that will evaluate as a numeric vector of numbers (zeros for bolus dosing).\" #>  #> $MC$formatting$action_dosing_durations$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$action_set_state_state #> $MC$formatting$action_set_state_state$width #> [1] 400 #>  #>  #> $MC$formatting$action_set_state_value #> $MC$formatting$action_set_state_value$width #> [1] 400 #>  #> $MC$formatting$action_set_state_value$placeholder #> [1] \"A value like 20 or an expression in terms of a system element such as Ac/2\" #>  #> $MC$formatting$action_set_state_value$tooltip #> [1] \"An expression that evaluates to a new number.\" #>  #>  #> $MC$formatting$action_manual_code #> $MC$formatting$action_manual_code$width #> [1] 400 #>  #> $MC$formatting$action_manual_code$height #> [1] 200 #>  #> $MC$formatting$action_manual_code$placeholder #> [1] \"SI_interval_ev = etRbind(SI_interval_ev,\\n  et(cmt  = 'Ac',\\n     id   = id,\\n     amt  = 10,\\n     evid = 4,\\n     time = time))\" #>  #> $MC$formatting$action_manual_code$tooltip #> [1] \"Expression with code to manually update the event table.\" #>  #>  #> $MC$formatting$rule_type #> $MC$formatting$rule_type$width #> [1] 300 #>  #> $MC$formatting$rule_type$options #> $MC$formatting$rule_type$options$dose #> $MC$formatting$rule_type$options$dose$choice #> [1] \"Dosing\" #>  #> $MC$formatting$rule_type$options$dose$subtext #> [1] \"Add bolus or infusion dosing\" #>  #> $MC$formatting$rule_type$options$dose$value #> [1] \"dose\" #>  #>  #> $MC$formatting$rule_type$options$state_reset #> $MC$formatting$rule_type$options$state_reset$choice #> [1] \"Set State\" #>  #> $MC$formatting$rule_type$options$state_reset$subtext #> [1] \"Reset the named state or compartment to a specified value\" #>  #> $MC$formatting$rule_type$options$state_reset$value #> [1] \"set state\" #>  #>  #> $MC$formatting$rule_type$options$manual #> $MC$formatting$rule_type$options$manual$choice #> [1] \"Manual\" #>  #> $MC$formatting$rule_type$options$manual$subtext #> [1] \"Alter the event table directly.\" #>  #> $MC$formatting$rule_type$options$manual$value #> [1] \"manual\" #>  #>  #>  #>  #> $MC$formatting$config_text #> $MC$formatting$config_text$width #> [1] 150 #>  #>  #> $MC$formatting$config_select #> $MC$formatting$config_select$width #> [1] 150 #>  #>  #> $MC$formatting$config_textarea #> $MC$formatting$config_textarea$width #> [1] 456 #>  #> $MC$formatting$config_textarea$height #> [1] 150 #>  #>  #> $MC$formatting$hot_current_rules #> $MC$formatting$hot_current_rules$width #> [1] 800 #>  #> $MC$formatting$hot_current_rules$height #> [1] 400 #>  #> $MC$formatting$hot_current_rules$no_rules #> [1] \"No Rules Defined\" #>  #>  #> $MC$formatting$hot_current_covariates #> $MC$formatting$hot_current_covariates$width #> [1] 800 #>  #> $MC$formatting$hot_current_covariates$height #> [1] 400 #>  #> $MC$formatting$hot_current_covariates$no_covariates #> [1] \"Covariate not yet defined\" #>  #>  #> $MC$formatting$nsub #> $MC$formatting$nsub$value #> [1] 10 #>  #> $MC$formatting$nsub$width #> [1] 50 #>  #> $MC$formatting$nsub$placeholder #> [1] 10 #>  #> $MC$formatting$nsub$tooltip #> [1] \"Number of sujbects in the current cohort.\" #>  #> $MC$formatting$nsub$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$visit_times #> $MC$formatting$visit_times$value #> [1] 0 #>  #> $MC$formatting$visit_times$width #> [1] 150 #>  #> $MC$formatting$visit_times$placeholder #> [1] \"c(0, 7, 14, 21)\" #>  #> $MC$formatting$visit_times$tooltip #> [1] \"Study visit times.\" #>  #> $MC$formatting$visit_times$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$trial_end #> $MC$formatting$trial_end$value #> [1] 100 #>  #> $MC$formatting$trial_end$width #> [1] 150 #>  #> $MC$formatting$trial_end$placeholder #> [1] \"c(0, 7, 14, 21)\" #>  #> $MC$formatting$trial_end$tooltip #> [1] \"Study visit times.\" #>  #> $MC$formatting$trial_end$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$covariates #> $MC$formatting$covariates$none_found #> [1] \"There were no coviaraites found in this model\" #>  #>  #> $MC$formatting$selected_covariate #> $MC$formatting$selected_covariate$width #> [1] 200 #>  #> $MC$formatting$selected_covariate$tooltip #> [1] \"Pick covariate to define.\" #>  #> $MC$formatting$selected_covariate$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$rule_name #> $MC$formatting$rule_name$width #> [1] 300 #>  #> $MC$formatting$rule_name$placeholder #> [1] \"first_dose\" #>  #> $MC$formatting$rule_name$tooltip #> [1] \"This is a unique identifier for the current rule. It should start with a letter and can contain letters, numbers and underscores.\" #>  #> $MC$formatting$rule_name$tooltip_position #> [1] \"bottom\" #>  #>  #> $MC$formatting$tc_dim #> $MC$formatting$tc_dim$width #> [1] 100 #>  #> $MC$formatting$tc_dim$default #> [1] \"d2x4\" #>  #> $MC$formatting$tc_dim$tooltip #> [1] \"rows x cols for figures\" #>  #> $MC$formatting$tc_dim$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$tc_dim$choices #> $MC$formatting$tc_dim$choices$d4x4 #> $MC$formatting$tc_dim$choices$d4x4$verb #> [1] \"4x4\" #>  #> $MC$formatting$tc_dim$choices$d4x4$nrow #> [1] 4 #>  #> $MC$formatting$tc_dim$choices$d4x4$ncol #> [1] 4 #>  #>  #> $MC$formatting$tc_dim$choices$d2x4 #> $MC$formatting$tc_dim$choices$d2x4$verb #> [1] \"2x4\" #>  #> $MC$formatting$tc_dim$choices$d2x4$nrow #> [1] 2 #>  #> $MC$formatting$tc_dim$choices$d2x4$ncol #> [1] 4 #>  #>  #> $MC$formatting$tc_dim$choices$d2x2 #> $MC$formatting$tc_dim$choices$d2x2$verb #> [1] \"2x2\" #>  #> $MC$formatting$tc_dim$choices$d2x2$nrow #> [1] 2 #>  #> $MC$formatting$tc_dim$choices$d2x2$ncol #> [1] 2 #>  #>  #> $MC$formatting$tc_dim$choices$d1x1 #> $MC$formatting$tc_dim$choices$d1x1$verb #> [1] \"1x1\" #>  #> $MC$formatting$tc_dim$choices$d1x1$nrow #> [1] 1 #>  #> $MC$formatting$tc_dim$choices$d1x1$ncol #> [1] 1 #>  #>  #>  #>  #> $MC$formatting$dvcols #> $MC$formatting$dvcols$width #> [1] 150 #>  #> $MC$formatting$dvcols$size #> [1] 10 #>  #> $MC$formatting$dvcols$tooltip #> [1] \"Select the different model outputs, states, etc to plot\" #>  #> $MC$formatting$dvcols$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$dvcols$choices #> $MC$formatting$dvcols$choices$outputs #> $MC$formatting$dvcols$choices$outputs$verb #> [1] \"Outputs\" #>  #>  #> $MC$formatting$dvcols$choices$states #> $MC$formatting$dvcols$choices$states$verb #> [1] \"States\" #>  #>  #> $MC$formatting$dvcols$choices$parameters #> $MC$formatting$dvcols$choices$parameters$verb #> [1] \"Parameters\" #>  #>  #> $MC$formatting$dvcols$choices$secondary #> $MC$formatting$dvcols$choices$secondary$verb #> [1] \"Secondary Parameters\" #>  #>  #> $MC$formatting$dvcols$choices$covariates #> $MC$formatting$dvcols$choices$covariates$verb #> [1] \"Covariates\" #>  #>  #>  #>  #> $MC$formatting$evplot #> $MC$formatting$evplot$width #> [1] 150 #>  #> $MC$formatting$evplot$tooltip #> [1] \"Select the events to plot\" #>  #> $MC$formatting$evplot$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$evplot$default #> [1] \"dose\" #>  #> $MC$formatting$evplot$choices #> $MC$formatting$evplot$choices$dose #> $MC$formatting$evplot$choices$dose$value #> [1] 1 #>  #> $MC$formatting$evplot$choices$dose$verb #> [1] \"Dose\" #>  #>  #> $MC$formatting$evplot$choices$set_state #> $MC$formatting$evplot$choices$set_state$value #> [1] 4 #>  #> $MC$formatting$evplot$choices$set_state$verb #> [1] \"State Resets\" #>  #>  #>  #>  #> $MC$formatting$fpage #> $MC$formatting$fpage$width #> [1] 50 #>  #> $MC$formatting$fpage$tooltip #> [1] \"Go to the selected output page\" #>  #> $MC$formatting$fpage$tooltip_position #> [1] \"top\" #>  #>  #>  #> $MC$labels #> $MC$labels$current_element #> NULL #>  #> $MC$labels$element_name #> NULL #>  #> $MC$labels$save_btn #> [1] \"Save\" #>  #> $MC$labels$runsim_btn #> [1] \"Simulate Cohort\" #>  #> $MC$labels$update_plot_btn #> [1] \"Update Plots\" #>  #> $MC$labels$clip_btn #> [1] \"Code\" #>  #> $MC$labels$copy_btn #> [1] \"Copy\" #>  #> $MC$labels$del_btn #> [1] \"Delete\" #>  #> $MC$labels$add_rule_btn #> [1] \"Add Rule\" #>  #> $MC$labels$add_cov_btn #> [1] \"Add Covariate Details\" #>  #> $MC$labels$new_btn #> [1] \"New\" #>  #> $MC$labels$source_model #> [1] \"Select Model\" #>  #> $MC$labels$dvcols #> NULL #>  #> $MC$labels$evplot #> NULL #>  #> $MC$labels$rule_type #> [1] \"Type of rule\" #>  #> $MC$labels$rule_condition #> [1] \"Condition to trigger rule\" #>  #> $MC$labels$action_dosing_state #> [1] \"Dosing into\" #>  #> $MC$labels$action_dosing_values #> [1] \"Values\" #>  #> $MC$labels$action_dosing_times #> [1] \"Times\" #>  #> $MC$labels$action_manual_code #> [1] \"Code to evaluate\" #>  #> $MC$labels$action_dosing_durations #> [1] \"Durations\" #>  #> $MC$labels$action_set_state_state #> [1] \"State/compartment to reset\" #>  #> $MC$labels$action_set_state_value #> [1] \"New value for state/compartment\" #>  #> $MC$labels$nsub #> [1] \"N Subs\" #>  #> $MC$labels$visit_times #> [1] \"Visit Times\" #>  #> $MC$labels$trial_end #> [1] \"End of Trial\" #>  #> $MC$labels$selected_covariate #> [1] \"Covariate\" #>  #> $MC$labels$covariate_type #> [1] \"Type/Distribution\" #>  #> $MC$labels$covariate_value #> [1] \"Values\" #>  #> $MC$labels$switch_output_interactive #> [1] \"Interactive\" #>  #> $MC$labels$switch_output_report #> [1] \"Report Preview\" #>  #> $MC$labels$tab_res_tc_figure #> [1] \"Timecourse\" #>  #> $MC$labels$tab_res_events_figure #> [1] \"Events\" #>  #> $MC$labels$tab_sim_env #> [1] \"Simulation Environment\" #>  #> $MC$labels$tab_chrt_ele_covs #> [1] \"Covariates\" #>  #> $MC$labels$tab_chrt_ele_rules #> [1] \"Create Rules\" #>  #> $MC$labels$tab_chrt_ele_trial #> [1] \"Trial Options\" #>  #> $MC$labels$tab_chrt_ele_sim #> [1] \"Simulation Options\" #>  #>  #> $MC$errors #> $MC$errors$no_sim_found #> [1] \"You need to run the simulation first.\" #>  #> $MC$errors$bad_sim #> [1] \"There was a problem with the simulation.\" #>  #> $MC$errors$cts_no_fig #> [1] \"There are no figures to display. You must run the simulation first\" #>  #>  #> $MC$tooltips #> $MC$tooltips$include #> [1] TRUE #>  #> $MC$tooltips$elements #> [1] \"Show cohort rules\" #>  #> $MC$tooltips$show_code #> [1] \"Show cohort code\" #>  #> $MC$tooltips$url_manual #> [1] \"https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html#example-manual-code-evaluation\" #>  #> $MC$tooltips$url_rule_env #> [1] \"https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html#rule-evaluation-environment\" #>  #>  #> $MC$rule_env_content #> [1] \"uiele = tagList(tags$b(\\\"Simulation environment\\\"), tags$br(),\\n                \\\"The condition and actions (e.g., dose times, amounts,\\n                durations) that are triggered are all\\n                evaluated in an environment with different objects that\\n                you can use. For details on this see the \\\", \\n                tags$a(href=state[[\\\"MC\\\"]][[\\\"tooltips\\\"]][[\\\"url_rule_env\\\"]],\\n                 \\\"Clinical Trial Simulation\\\"), \\\" vignette.\\\")\\nuiele = tagList(uiele, \\\"For the current system the following are available:\\\", tags$br())\\nuiele = tagList(uiele, tags$br())\\nuiele = tagList(uiele, rx_details[[\\\"ht_info\\\"]])\" #>  #> $MC$sim_config #> $MC$sim_config$atol #> $MC$sim_config$atol$group #> [1] \"ODE Solver\" #>  #> $MC$sim_config$atol$label #> [1] \"Absolute tolerance\" #>  #> $MC$sim_config$atol$tooltip #> [1] \"Absolute tolerance used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $MC$sim_config$atol$uitype #> [1] \"text\" #>  #> $MC$sim_config$atol$type #> [1] \"numeric\" #>  #> $MC$sim_config$atol$value #> [1] \"1e-8\" #>  #> $MC$sim_config$atol$options #> NULL #>  #> $MC$sim_config$atol$use #> [1] \"rxSolve\" #>  #> $MC$sim_config$atol$placeholder #> [1] \"1e-8\" #>  #>  #> $MC$sim_config$rtol #> $MC$sim_config$rtol$group #> [1] \"ODE Solver\" #>  #> $MC$sim_config$rtol$label #> [1] \"Relative tolerance\" #>  #> $MC$sim_config$rtol$tooltip #> [1] \"Relative tolerance used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $MC$sim_config$rtol$uitype #> [1] \"text\" #>  #> $MC$sim_config$rtol$type #> [1] \"numeric\" #>  #> $MC$sim_config$rtol$value #> [1] \"1e-6\" #>  #> $MC$sim_config$rtol$options #> NULL #>  #> $MC$sim_config$rtol$use #> [1] \"rxSolve\" #>  #> $MC$sim_config$rtol$placeholder #> [1] \"1e-6\" #>  #>  #> $MC$sim_config$sigdig #> $MC$sim_config$sigdig$group #> [1] \"ODE Solver\" #>  #> $MC$sim_config$sigdig$label #> [1] \"Significant digits\" #>  #> $MC$sim_config$sigdig$tooltip #> [1] \"Specifies the \\\"significant digits\\\" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\\\\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\\\\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\\\\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.\" #>  #> $MC$sim_config$sigdig$uitype #> [1] \"text\" #>  #> $MC$sim_config$sigdig$type #> [1] \"numeric\" #>  #> $MC$sim_config$sigdig$value #> [1] \"NULL\" #>  #> $MC$sim_config$sigdig$options #> NULL #>  #> $MC$sim_config$sigdig$use #> [1] \"rxSolve\" #>  #> $MC$sim_config$sigdig$placeholder #> [1] \"NULL\" #>  #>  #> $MC$sim_config$seed #> $MC$sim_config$seed$group #> [1] \"Misc\" #>  #> $MC$sim_config$seed$label #> [1] \"Random Seed\" #>  #> $MC$sim_config$seed$tooltip #> [1] \"Integer value to set the seed for the random number generator\" #>  #> $MC$sim_config$seed$uitype #> [1] \"text\" #>  #> $MC$sim_config$seed$type #> [1] \"numeric\" #>  #> $MC$sim_config$seed$value #> [1] \"86753090\" #>  #> $MC$sim_config$seed$options #> NULL #>  #> $MC$sim_config$seed$use #> [1] \"Misc\" #>  #> $MC$sim_config$seed$placeholder #> [1] \"8675309\" #>  #>  #> $MC$sim_config$nsteps #> $MC$sim_config$nsteps$group #> [1] \"Misc\" #>  #> $MC$sim_config$nsteps$label #> [1] \"Time Steps\" #>  #> $MC$sim_config$nsteps$tooltip #> [1] \"Integer value representing the number of time steps to add between zero and the end of trial. A larger value will give smoother curves but it will also take more time to simulate.\" #>  #> $MC$sim_config$nsteps$uitype #> [1] \"text\" #>  #> $MC$sim_config$nsteps$type #> [1] \"numeric\" #>  #> $MC$sim_config$nsteps$value #> [1] \"200\" #>  #> $MC$sim_config$nsteps$options #> NULL #>  #> $MC$sim_config$nsteps$use #> [1] \"Misc\" #>  #> $MC$sim_config$nsteps$placeholder #> [1] \"200\" #>  #>  #> $MC$sim_config$preamble #> $MC$sim_config$preamble$group #> [1] \"Rule Environment Preamble\" #>  #> $MC$sim_config$preamble$label #> NULL #>  #> $MC$sim_config$preamble$tooltip #> [1] \"Optional user-defined code to be executed in the rule-evaluation environment. You can create user-defined functions here.\" #>  #> $MC$sim_config$preamble$uitype #> [1] \"textarea\" #>  #> $MC$sim_config$preamble$type #> [1] \"character\" #>  #> $MC$sim_config$preamble$value #> NULL #>  #> $MC$sim_config$preamble$options #> NULL #>  #> $MC$sim_config$preamble$use #> [1] \"rules\" #>  #> $MC$sim_config$preamble$placeholder #> [1] \"my_func = function(id=NULL){\\n\\n}\" #>  #>  #>  #> $MC$covariate_generation #> $MC$covariate_generation$tooltip_position #> [1] \"right\" #>  #> $MC$covariate_generation$width #> [1] 200 #>  #> $MC$covariate_generation$types #> $MC$covariate_generation$types$discrete #> $MC$covariate_generation$types$discrete$choice #> [1] \"Select from discrete values\" #>  #> $MC$covariate_generation$types$discrete$type #> [1] \"discrete\" #>  #> $MC$covariate_generation$types$discrete$placeholder #> [1] \"0, 1, 2\" #>  #> $MC$covariate_generation$types$discrete$tooltip #> [1] \"Provide a list of possible discrete values and these will be randomly selected when genreating subjects.\" #>  #>  #> $MC$covariate_generation$types$fixed #> $MC$covariate_generation$types$fixed$choice #> [1] \"Fix at value\" #>  #> $MC$covariate_generation$types$fixed$type #> [1] \"fixed\" #>  #> $MC$covariate_generation$types$fixed$placeholder #> [1] \"1\" #>  #> $MC$covariate_generation$types$fixed$tooltip #> [1] \"Fix the covariate at a specific value.\" #>  #>  #> $MC$covariate_generation$types$cont_random #> $MC$covariate_generation$types$cont_random$choice #> [1] \"Continuous/random in range\" #>  #> $MC$covariate_generation$types$cont_random$type #> [1] \"continuous\" #>  #> $MC$covariate_generation$types$cont_random$sampling #> [1] \"random\" #>  #> $MC$covariate_generation$types$cont_random$placeholder #> [1] \"0,1\" #>  #> $MC$covariate_generation$types$cont_random$tooltip #> [1] \"Sample randomly from within the specified range.\" #>  #>  #> $MC$covariate_generation$types$cont_normal #> $MC$covariate_generation$types$cont_normal$choice #> [1] \"Sample normally\" #>  #> $MC$covariate_generation$types$cont_normal$type #> [1] \"continuous\" #>  #> $MC$covariate_generation$types$cont_normal$sampling #> [1] \"normal\" #>  #> $MC$covariate_generation$types$cont_normal$placeholder #> [1] \"mean, variance\" #>  #> $MC$covariate_generation$types$cont_normal$tooltip #> [1] \"Sample from normal distribution provided a mean and variance\" #>  #>  #> $MC$covariate_generation$types$cont_lognormal #> $MC$covariate_generation$types$cont_lognormal$choice #> [1] \"Sample log-normally\" #>  #> $MC$covariate_generation$types$cont_lognormal$type #> [1] \"continuous\" #>  #> $MC$covariate_generation$types$cont_lognormal$sampling #> [1] \"log-normal\" #>  #> $MC$covariate_generation$types$cont_lognormal$placeholder #> [1] \"mean, variance\" #>  #> $MC$covariate_generation$types$cont_lognormal$tooltip #> [1] \"Sample from log-normal distribution provided a mean and variance\" #>  #>  #>  #>  #>  #> $CTS #> $CTS$button_counters #> $CTS$button_counters$button_clk_runsim #> [1] 0 #>  #> $CTS$button_counters$button_clk_update_plot #> [1] 0 #>  #> $CTS$button_counters$button_clk_save #> [1] 0 #>  #> $CTS$button_counters$button_clk_clip #> [1] 0 #>  #> $CTS$button_counters$button_clk_del #> [1] 0 #>  #> $CTS$button_counters$button_clk_copy #> [1] 0 #>  #> $CTS$button_counters$button_clk_new #> [1] 0 #>  #> $CTS$button_counters$button_clk_add_cov #> [1] 0 #>  #> $CTS$button_counters$button_clk_add_rule #> [1] 0 #>  #>  #> $CTS$ui_hold #> $CTS$ui_hold$button_clk_runsim #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_update_plot #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_save #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_clip #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_del #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_copy #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_new #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_add_cov #> [1] FALSE #>  #> $CTS$ui_hold$button_clk_add_rule #> [1] FALSE #>  #> $CTS$ui_hold$element_name #> [1] FALSE #>  #> $CTS$ui_hold$nsub #> [1] FALSE #>  #> $CTS$ui_hold$fpage #> [1] FALSE #>  #> $CTS$ui_hold$dvcols #> [1] FALSE #>  #> $CTS$ui_hold$tc_dim #> [1] FALSE #>  #> $CTS$ui_hold$evplot #> [1] FALSE #>  #> $CTS$ui_hold$visit_times #> [1] FALSE #>  #> $CTS$ui_hold$trial_end #> [1] FALSE #>  #> $CTS$ui_hold$rule_condition #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_state #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_values #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_times #> [1] FALSE #>  #> $CTS$ui_hold$action_dosing_durations #> [1] FALSE #>  #> $CTS$ui_hold$action_set_state_state #> [1] FALSE #>  #> $CTS$ui_hold$action_set_state_value #> [1] FALSE #>  #> $CTS$ui_hold$action_manual_code #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_atol #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_rtol #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_sigdig #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_seed #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_nsteps #> [1] FALSE #>  #> $CTS$ui_hold$cts_config_preamble #> [1] FALSE #>  #> $CTS$ui_hold$rule_type #> [1] FALSE #>  #> $CTS$ui_hold$rule_name #> [1] FALSE #>  #> $CTS$ui_hold$source_model #> [1] FALSE #>  #> $CTS$ui_hold$hot_current_rules #> [1] FALSE #>  #> $CTS$ui_hold$covariate_type_selected #> [1] FALSE #>  #> $CTS$ui_hold$covariate_value #> [1] FALSE #>  #> $CTS$ui_hold$selected_covariate #> [1] FALSE #>  #> $CTS$ui_hold$switch_output #> [1] FALSE #>  #> $CTS$ui_hold$element_selection #> [1] FALSE #>  #>  #> $CTS$ui_ids #>  [1] \"button_clk_runsim\"       \"button_clk_update_plot\"  #>  [3] \"button_clk_save\"         \"button_clk_clip\"         #>  [5] \"button_clk_del\"          \"button_clk_copy\"         #>  [7] \"button_clk_new\"          \"button_clk_add_cov\"      #>  [9] \"button_clk_add_rule\"     \"element_name\"            #> [11] \"nsub\"                    \"fpage\"                   #> [13] \"dvcols\"                  \"tc_dim\"                  #> [15] \"evplot\"                  \"visit_times\"             #> [17] \"trial_end\"               \"rule_condition\"          #> [19] \"action_dosing_state\"     \"action_dosing_values\"    #> [21] \"action_dosing_times\"     \"action_dosing_durations\" #> [23] \"action_set_state_state\"  \"action_set_state_value\"  #> [25] \"action_manual_code\"      \"cts_config_atol\"         #> [27] \"cts_config_rtol\"         \"cts_config_sigdig\"       #> [29] \"cts_config_seed\"         \"cts_config_nsteps\"       #> [31] \"cts_config_preamble\"     \"rule_type\"               #> [33] \"rule_name\"               \"source_model\"            #> [35] \"hot_current_rules\"       \"covariate_type_selected\" #> [37] \"covariate_value\"         \"selected_covariate\"      #> [39] \"switch_output\"           \"element_selection\"       #>  #> $CTS$isgood #> [1] TRUE #>  #> $CTS$MDL #> $CTS$MDL$isgood #> [1] TRUE #>  #> $CTS$MDL$hasmdl #> [1] TRUE #>  #> $CTS$MDL$catalog #>                   label      object MOD_TYPE id #> 1 One compartment model MB_obj_1_rx       MB MB #> 2          PK Biomarker MB_obj_2_rx       MB MB #>                           checksum                      MDLchecksum #> 1 faf5d08a8606e06f198cb8a233bcad72 04c4a233276d3bd6cbefe58cce05fbd1 #> 2 faf5d08a8606e06f198cb8a233bcad72 f3dd1904f0f09fe0ed4d13347a620749 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    code #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    MB_obj_1_fcn = function () \\n{\\n    description <- \"One compartment PK model with linear clearance using differential equations\"\\n    ini({\\n        lka <- 0.45\\n        label(\"Absorption rate (Ka)\")\\n        lcl <- 1\\n        label(\"Clearance (CL)\")\\n        lvc <- 3.45\\n        label(\"Central volume of distribution (V)\")\\n        propSd <- c(0, 0.5)\\n        label(\"Proportional residual error (fraction)\")\\n        etalcl ~ 0.1\\n    })\\n    model({\\n        ka <- exp(lka)\\n        cl <- exp(lcl + etalcl)\\n        vc <- exp(lvc)\\n        kel <- cl/vc\\n        d/dt(depot) <- -ka * depot\\n        d/dt(central) <- ka * depot - kel * central\\n        Cc <- central/vc\\n        Cc ~ prop(propSd)\\n    })\\n}\\nMB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #> 2 MB_obj_2_fcn = function () \\n{\\n    ini({\\n        TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf)\\n        TV_ka <- c(2.22044604925031e-16, 0.282, Inf)\\n        TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\\n        TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\\n        TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\\n        TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\\n        TV_MW <- fix(2.22044604925031e-16, 140, Inf)\\n        TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf)\\n        TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf)\\n        TV_Emax <- c(2.22044604925031e-16, 100, Inf)\\n        TV_EC50 <- fix(2.22044604925031e-16, 300, Inf)\\n        prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_BM <- c(2.22044604925031e-16, 0.1, Inf)\\n        ETAka ~ 0.416\\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \\n            0.0377, 0.0789)\\n        ETAQ ~ 0.699\\n        ETABM_IC ~ 0.05\\n        ETAEmax ~ 0.1\\n    })\\n    model({\\n        F1 = TV_F1\\n        ka = TV_ka * exp(ETAka)\\n        CL = exp(TV_CL) * exp(ETACL)\\n        Vc = exp(TV_Vc) * exp(ETAVc)\\n        Cc = Ac/(Vc)\\n        Vp = exp(TV_Vp) * exp(ETAVp)\\n        Q = exp(TV_Q) * exp(ETAQ)\\n        MW = TV_MW\\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\\n        kdeg_BM = TV_kdeg_BM\\n        Emax = TV_Emax * exp(ETAEmax)\\n        EC50 = TV_EC50\\n        Dinf = 0\\n        WTTV = 70\\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \\n            0.1)\\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\\n        ksyn_BM = kdeg_BM * BM_IC\\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\\n        BM(0) = BM_IC\\n        d/dt(At) = (-ka * At)\\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \\n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\\n        C_ng_ml = Cc * MW\\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\\n        BM_obs = BM\\n        BM_obs ~ add(add_err_BM)\\n    })\\n}\\nMB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>  #> $CTS$MDL$modules #> $CTS$MDL$modules$MB #> $CTS$MDL$modules$MB$MB #> [1] \"faf5d08a8606e06f198cb8a233bcad72\" #>  #>  #>  #> $CTS$MDL$mdl #> $CTS$MDL$mdl$MB_obj_1_rx #> $CTS$MDL$mdl$MB_obj_1_rx$label #> [1] \"One compartment model\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$MOD_TYPE #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$id #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$rx_obj #>  ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>    lka    lcl    lvc propSd  #>   0.45   1.00   3.45   0.50  #>  #> Omega ($omega):  #>        etalcl #> etalcl    0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1            depot #> 2                  2          central #>  ── μ-referencing ($muRefTable): ──   #>   theta    eta level #> 1   lcl etalcl    id #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     description <- \"One compartment PK model with linear clearance using differential equations\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>         etalcl ~ 0.1 #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl + etalcl) #>         vc <- exp(lvc) #>         kel <- cl/vc #>         d/dt(depot) <- -ka * depot #>         d/dt(central) <- ka * depot - kel * central #>         Cc <- central/vc #>         Cc ~ prop(propSd) #>     }) #> } #>  #> $CTS$MDL$mdl$MB_obj_1_rx$rx_obj_name #> [1] \"MB_obj_1_rx\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$fcn_def #> [1] \"function () \\n{\\n    description <- \\\"One compartment PK model with linear clearance using differential equations\\\"\\n    ini({\\n        lka <- 0.45\\n        label(\\\"Absorption rate (Ka)\\\")\\n        lcl <- 1\\n        label(\\\"Clearance (CL)\\\")\\n        lvc <- 3.45\\n        label(\\\"Central volume of distribution (V)\\\")\\n        propSd <- c(0, 0.5)\\n        label(\\\"Proportional residual error (fraction)\\\")\\n        etalcl ~ 0.1\\n    })\\n    model({\\n        ka <- exp(lka)\\n        cl <- exp(lcl + etalcl)\\n        vc <- exp(lvc)\\n        kel <- cl/vc\\n        d/dt(depot) <- -ka * depot\\n        d/dt(central) <- ka * depot - kel * central\\n        Cc <- central/vc\\n        Cc ~ prop(propSd)\\n    })\\n}\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$MDLMETA #> [1] \"base model\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$code #> [1] \"MB_obj_1_fcn = function () \\n{\\n    description <- \\\"One compartment PK model with linear clearance using differential equations\\\"\\n    ini({\\n        lka <- 0.45\\n        label(\\\"Absorption rate (Ka)\\\")\\n        lcl <- 1\\n        label(\\\"Clearance (CL)\\\")\\n        lvc <- 3.45\\n        label(\\\"Central volume of distribution (V)\\\")\\n        propSd <- c(0, 0.5)\\n        label(\\\"Proportional residual error (fraction)\\\")\\n        etalcl ~ 0.1\\n    })\\n    model({\\n        ka <- exp(lka)\\n        cl <- exp(lcl + etalcl)\\n        vc <- exp(lvc)\\n        kel <- cl/vc\\n        d/dt(depot) <- -ka * depot\\n        d/dt(central) <- ka * depot - kel * central\\n        Cc <- central/vc\\n        Cc ~ prop(propSd)\\n    })\\n}\\nMB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn)\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$checksum #> [1] \"faf5d08a8606e06f198cb8a233bcad72\" #>  #> $CTS$MDL$mdl$MB_obj_1_rx$MDLchecksum #> [1] \"04c4a233276d3bd6cbefe58cce05fbd1\" #>  #>  #> $CTS$MDL$mdl$MB_obj_2_rx #> $CTS$MDL$mdl$MB_obj_2_rx$label #> [1] \"PK Biomarker\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$MOD_TYPE #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$id #> [1] \"MB\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$rx_obj #>  ── rxode2-based free-form 4-cmt ODE model ──────────────────────────────────────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>        TV_F1        TV_ka        TV_CL        TV_Vc        TV_Vp         TV_Q  #>    0.7440000    0.2820000   -1.6094379    1.2837078    1.0116009   -0.2916901  #>        TV_MW     TV_BM_IC   TV_kdeg_BM      TV_Emax      TV_EC50  prop_err_PK  #>  140.0000000 1000.0000000    0.1000000  100.0000000  300.0000000    0.1000000  #>   add_err_PK   add_err_BM  #>    0.1000000    0.1000000  #>  #> Omega ($omega):  #>          ETAka   ETACL  ETAVc  ETAVp  ETAQ ETABM_IC ETAEmax #> ETAka    0.416 0.00000 0.0000 0.0000 0.000     0.00     0.0 #> ETACL    0.000 0.09875 0.0786 0.0619 0.000     0.00     0.0 #> ETAVc    0.000 0.07860 0.1160 0.0377 0.000     0.00     0.0 #> ETAVp    0.000 0.06190 0.0377 0.0789 0.000     0.00     0.0 #> ETAQ     0.000 0.00000 0.0000 0.0000 0.699     0.00     0.0 #> ETABM_IC 0.000 0.00000 0.0000 0.0000 0.000     0.05     0.0 #> ETAEmax  0.000 0.00000 0.0000 0.0000 0.000     0.00     0.1 #>  #> States ($state or $stateDf):  #>   Compartment Number Compartment Name #> 1                  1               At #> 2                  2               Ac #> 3                  3               Cp #> 4                  4               BM #>  ── Multiple Endpoint Model ($multipleEndpoint): ──   #>      variable                    cmt                    dvid* #> 1 C_ng_ml ~ … cmt='C_ng_ml' or cmt=5 dvid='C_ng_ml' or dvid=1 #> 2  BM_obs ~ …  cmt='BM_obs' or cmt=6  dvid='BM_obs' or dvid=2 #>   * If dvids are outside this range, all dvids are re-numered sequentially, ie 1,7, 10 becomes 1,2,3 etc #>  #>  ── Model (Normalized Syntax): ──  #> function() { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #>  #> $CTS$MDL$mdl$MB_obj_2_rx$rx_obj_name #> [1] \"MB_obj_2_rx\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$fcn_def #> [1] \"function () \\n{\\n    ini({\\n        TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf)\\n        TV_ka <- c(2.22044604925031e-16, 0.282, Inf)\\n        TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\\n        TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\\n        TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\\n        TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\\n        TV_MW <- fix(2.22044604925031e-16, 140, Inf)\\n        TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf)\\n        TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf)\\n        TV_Emax <- c(2.22044604925031e-16, 100, Inf)\\n        TV_EC50 <- fix(2.22044604925031e-16, 300, Inf)\\n        prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_BM <- c(2.22044604925031e-16, 0.1, Inf)\\n        ETAka ~ 0.416\\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \\n            0.0377, 0.0789)\\n        ETAQ ~ 0.699\\n        ETABM_IC ~ 0.05\\n        ETAEmax ~ 0.1\\n    })\\n    model({\\n        F1 = TV_F1\\n        ka = TV_ka * exp(ETAka)\\n        CL = exp(TV_CL) * exp(ETACL)\\n        Vc = exp(TV_Vc) * exp(ETAVc)\\n        Cc = Ac/(Vc)\\n        Vp = exp(TV_Vp) * exp(ETAVp)\\n        Q = exp(TV_Q) * exp(ETAQ)\\n        MW = TV_MW\\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\\n        kdeg_BM = TV_kdeg_BM\\n        Emax = TV_Emax * exp(ETAEmax)\\n        EC50 = TV_EC50\\n        Dinf = 0\\n        WTTV = 70\\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \\n            0.1)\\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\\n        ksyn_BM = kdeg_BM * BM_IC\\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\\n        BM(0) = BM_IC\\n        d/dt(At) = (-ka * At)\\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \\n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\\n        C_ng_ml = Cc * MW\\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\\n        BM_obs = BM\\n        BM_obs ~ add(add_err_BM)\\n    })\\n}\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$MDLMETA #> [1] \"base model\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$code #> [1] \"MB_obj_2_fcn = function () \\n{\\n    ini({\\n        TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf)\\n        TV_ka <- c(2.22044604925031e-16, 0.282, Inf)\\n        TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384)\\n        TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384)\\n        TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384)\\n        TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384)\\n        TV_MW <- fix(2.22044604925031e-16, 140, Inf)\\n        TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf)\\n        TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf)\\n        TV_Emax <- c(2.22044604925031e-16, 100, Inf)\\n        TV_EC50 <- fix(2.22044604925031e-16, 300, Inf)\\n        prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_PK <- c(2.22044604925031e-16, 0.1, Inf)\\n        add_err_BM <- c(2.22044604925031e-16, 0.1, Inf)\\n        ETAka ~ 0.416\\n        ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619, \\n            0.0377, 0.0789)\\n        ETAQ ~ 0.699\\n        ETABM_IC ~ 0.05\\n        ETAEmax ~ 0.1\\n    })\\n    model({\\n        F1 = TV_F1\\n        ka = TV_ka * exp(ETAka)\\n        CL = exp(TV_CL) * exp(ETACL)\\n        Vc = exp(TV_Vc) * exp(ETAVc)\\n        Cc = Ac/(Vc)\\n        Vp = exp(TV_Vp) * exp(ETAVp)\\n        Q = exp(TV_Q) * exp(ETAQ)\\n        MW = TV_MW\\n        BM_IC = TV_BM_IC * exp(ETABM_IC)\\n        kdeg_BM = TV_kdeg_BM\\n        Emax = TV_Emax * exp(ETAEmax)\\n        EC50 = TV_EC50\\n        Dinf = 0\\n        WTTV = 70\\n        CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID * \\n            0.1)\\n        kel = CL_IND/Vc * ((WT/WTTV))^(-0.35)\\n        kcp = Q/Vc * ((WT/WTTV))^(-0.35)\\n        kpc = Q/Vp * ((WT/WTTV))^(-0.35)\\n        ksyn_BM = kdeg_BM * BM_IC\\n        STIM = 1 + Emax * Cc/(EC50 + Cc)\\n        BM(0) = BM_IC\\n        d/dt(At) = (-ka * At)\\n        d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc + \\n            kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc\\n        d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp)\\n        d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM)\\n        C_ng_ml = Cc * MW\\n        C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK)\\n        BM_obs = BM\\n        BM_obs ~ add(add_err_BM)\\n    })\\n}\\nMB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn)\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$checksum #> [1] \"faf5d08a8606e06f198cb8a233bcad72\" #>  #> $CTS$MDL$mdl$MB_obj_2_rx$MDLchecksum #> [1] \"f3dd1904f0f09fe0ed4d13347a620749\" #>  #>  #>  #>  #> $CTS$ui_ele #>  [1] \"element_name\"            \"nsub\"                    #>  [3] \"fpage\"                   \"dvcols\"                  #>  [5] \"tc_dim\"                  \"evplot\"                  #>  [7] \"visit_times\"             \"trial_end\"               #>  [9] \"rule_condition\"          \"action_dosing_state\"     #> [11] \"action_dosing_values\"    \"action_dosing_times\"     #> [13] \"action_dosing_durations\" \"action_set_state_state\"  #> [15] \"action_set_state_value\"  \"action_manual_code\"      #> [17] \"cts_config_atol\"         \"cts_config_rtol\"         #> [19] \"cts_config_sigdig\"       \"cts_config_seed\"         #> [21] \"cts_config_nsteps\"       \"cts_config_preamble\"     #>  #> $CTS$element_cntr #> [1] 1 #>  #> $CTS$sc_meta #> $CTS$sc_meta$config #> $CTS$sc_meta$config$atol #> $CTS$sc_meta$config$atol$group #> [1] \"ODE Solver\" #>  #> $CTS$sc_meta$config$atol$label #> [1] \"Absolute tolerance\" #>  #> $CTS$sc_meta$config$atol$tooltip #> [1] \"Absolute tolerance used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $CTS$sc_meta$config$atol$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$atol$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$atol$value #> [1] \"1e-8\" #>  #> $CTS$sc_meta$config$atol$options #> NULL #>  #> $CTS$sc_meta$config$atol$use #> [1] \"rxSolve\" #>  #> $CTS$sc_meta$config$atol$placeholder #> [1] \"1e-8\" #>  #> $CTS$sc_meta$config$atol$ui #> [1] \"cts_config_atol\" #>  #>  #> $CTS$sc_meta$config$rtol #> $CTS$sc_meta$config$rtol$group #> [1] \"ODE Solver\" #>  #> $CTS$sc_meta$config$rtol$label #> [1] \"Relative tolerance\" #>  #> $CTS$sc_meta$config$rtol$tooltip #> [1] \"Relative tolerance used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution.\" #>  #> $CTS$sc_meta$config$rtol$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$rtol$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$rtol$value #> [1] \"1e-6\" #>  #> $CTS$sc_meta$config$rtol$options #> NULL #>  #> $CTS$sc_meta$config$rtol$use #> [1] \"rxSolve\" #>  #> $CTS$sc_meta$config$rtol$placeholder #> [1] \"1e-6\" #>  #> $CTS$sc_meta$config$rtol$ui #> [1] \"cts_config_rtol\" #>  #>  #> $CTS$sc_meta$config$sigdig #> $CTS$sc_meta$config$sigdig$group #> [1] \"ODE Solver\" #>  #> $CTS$sc_meta$config$sigdig$label #> [1] \"Significant digits\" #>  #> $CTS$sc_meta$config$sigdig$tooltip #> [1] \"Specifies the \\\"significant digits\\\" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\\\\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\\\\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\\\\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.\" #>  #> $CTS$sc_meta$config$sigdig$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$sigdig$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$sigdig$value #> [1] \"NULL\" #>  #> $CTS$sc_meta$config$sigdig$options #> NULL #>  #> $CTS$sc_meta$config$sigdig$use #> [1] \"rxSolve\" #>  #> $CTS$sc_meta$config$sigdig$placeholder #> [1] \"NULL\" #>  #> $CTS$sc_meta$config$sigdig$ui #> [1] \"cts_config_sigdig\" #>  #>  #> $CTS$sc_meta$config$seed #> $CTS$sc_meta$config$seed$group #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$seed$label #> [1] \"Random Seed\" #>  #> $CTS$sc_meta$config$seed$tooltip #> [1] \"Integer value to set the seed for the random number generator\" #>  #> $CTS$sc_meta$config$seed$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$seed$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$seed$value #> [1] \"86753090\" #>  #> $CTS$sc_meta$config$seed$options #> NULL #>  #> $CTS$sc_meta$config$seed$use #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$seed$placeholder #> [1] \"8675309\" #>  #> $CTS$sc_meta$config$seed$ui #> [1] \"cts_config_seed\" #>  #>  #> $CTS$sc_meta$config$nsteps #> $CTS$sc_meta$config$nsteps$group #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$nsteps$label #> [1] \"Time Steps\" #>  #> $CTS$sc_meta$config$nsteps$tooltip #> [1] \"Integer value representing the number of time steps to add between zero and the end of trial. A larger value will give smoother curves but it will also take more time to simulate.\" #>  #> $CTS$sc_meta$config$nsteps$uitype #> [1] \"text\" #>  #> $CTS$sc_meta$config$nsteps$type #> [1] \"numeric\" #>  #> $CTS$sc_meta$config$nsteps$value #> [1] \"200\" #>  #> $CTS$sc_meta$config$nsteps$options #> NULL #>  #> $CTS$sc_meta$config$nsteps$use #> [1] \"Misc\" #>  #> $CTS$sc_meta$config$nsteps$placeholder #> [1] \"200\" #>  #> $CTS$sc_meta$config$nsteps$ui #> [1] \"cts_config_nsteps\" #>  #>  #> $CTS$sc_meta$config$preamble #> $CTS$sc_meta$config$preamble$group #> [1] \"Rule Environment Preamble\" #>  #> $CTS$sc_meta$config$preamble$label #> NULL #>  #> $CTS$sc_meta$config$preamble$tooltip #> [1] \"Optional user-defined code to be executed in the rule-evaluation environment. You can create user-defined functions here.\" #>  #> $CTS$sc_meta$config$preamble$uitype #> [1] \"textarea\" #>  #> $CTS$sc_meta$config$preamble$type #> [1] \"character\" #>  #> $CTS$sc_meta$config$preamble$value #> NULL #>  #> $CTS$sc_meta$config$preamble$options #> NULL #>  #> $CTS$sc_meta$config$preamble$use #> [1] \"rules\" #>  #> $CTS$sc_meta$config$preamble$placeholder #> [1] \"my_func = function(id=NULL){\\n\\n}\" #>  #> $CTS$sc_meta$config$preamble$ui #> [1] \"cts_config_preamble\" #>  #>  #>  #> $CTS$sc_meta$cfg_summary #>       name                  ui     use      type                     group #> 1     atol     cts_config_atol rxSolve   numeric                ODE Solver #> 2     rtol     cts_config_rtol rxSolve   numeric                ODE Solver #> 3   sigdig   cts_config_sigdig rxSolve   numeric                ODE Solver #> 4     seed     cts_config_seed    Misc   numeric                      Misc #> 5   nsteps   cts_config_nsteps    Misc   numeric                      Misc #> 6 preamble cts_config_preamble   rules character Rule Environment Preamble #>  #> $CTS$sc_meta$ui_config #> [1] \"cts_config_atol\"     \"cts_config_rtol\"     \"cts_config_sigdig\"   #> [4] \"cts_config_seed\"     \"cts_config_nsteps\"   \"cts_config_preamble\" #>  #>  #> $CTS$elements #> $CTS$elements$element_1 #> $CTS$elements$element_1$isgood #> [1] TRUE #>  #> $CTS$elements$element_1$ui #> $CTS$elements$element_1$ui$element_name #> [1] \"cohort 1\" #>  #> $CTS$elements$element_1$ui$nsub #> [1] 10 #>  #> $CTS$elements$element_1$ui$visit_times #> [1] 0 #>  #> $CTS$elements$element_1$ui$trial_end #> [1] 100 #>  #> $CTS$elements$element_1$ui$fpage #> [1] \"1\" #>  #> $CTS$elements$element_1$ui$dvcols #> [1] \"\" #>  #> $CTS$elements$element_1$ui$tc_dim #> [1] \"d2x4\" #>  #> $CTS$elements$element_1$ui$evplot #> [1] 1 #>  #> $CTS$elements$element_1$ui$source_model #> [1] \"MB_obj_1_rx\" #>  #> $CTS$elements$element_1$ui$cts_config_atol #> [1] \"1e-8\" #>  #> $CTS$elements$element_1$ui$cts_config_rtol #> [1] \"1e-6\" #>  #> $CTS$elements$element_1$ui$cts_config_sigdig #> [1] \"NULL\" #>  #> $CTS$elements$element_1$ui$cts_config_seed #> [1] \"86753090\" #>  #> $CTS$elements$element_1$ui$cts_config_nsteps #> [1] \"200\" #>  #>  #> $CTS$elements$element_1$id #> [1] \"element_1\" #>  #> $CTS$elements$element_1$idx #> [1] 1 #>  #> $CTS$elements$element_1$element_object_name #> [1] \"CTS_obj_1\" #>  #> $CTS$elements$element_1$cov_object_name #> [1] \"CTS_obj_1_cov\" #>  #> $CTS$elements$element_1$rules_object_name #> [1] \"CTS_obj_1_rules\" #>  #> $CTS$elements$element_1$subs_object_name #> [1] \"CTS_obj_1_subs\" #>  #> $CTS$elements$element_1$rxopts_object_name #> [1] \"CTS_obj_1_rxopts\" #>  #> $CTS$elements$element_1$simres_object_name #> [1] \"CTS_obj_1_simres\" #>  #> $CTS$elements$element_1$ot_object_name #> [1] \"CTS_obj_1_output_times\" #>  #> $CTS$elements$element_1$fgtc_object_name #> [1] \"CTS_obj_1_fgtc\" #>  #> $CTS$elements$element_1$fgev_object_name #> [1] \"CTS_obj_1_fgev\" #>  #> $CTS$elements$element_1$code_previous #> [1] \"\" #>  #> $CTS$elements$element_1$rx_details #> $CTS$elements$element_1$rx_details$isgood #> [1] TRUE #>  #> $CTS$elements$element_1$rx_details$msgs #> NULL #>  #> $CTS$elements$element_1$rx_details$elements #> $CTS$elements$element_1$rx_details$elements$covariates #> character(0) #>  #> $CTS$elements$element_1$rx_details$elements$population #> [1] \"lka\" \"lcl\" \"lvc\" #>  #> $CTS$elements$element_1$rx_details$elements$parameters #> [1] \"ka\" \"cl\" \"vc\" #>  #> $CTS$elements$element_1$rx_details$elements$secondary #> [1] \"kel\" #>  #> $CTS$elements$element_1$rx_details$elements$residual_error #> [1] \"propSd\" #>  #> $CTS$elements$element_1$rx_details$elements$iiv #> [1] \"etalcl\" #>  #> $CTS$elements$element_1$rx_details$elements$outputs #> [1] \"Cc\" #>  #> $CTS$elements$element_1$rx_details$elements$states #> [1] \"depot\"   \"central\" #>  #>  #> $CTS$elements$element_1$rx_details$txt_info #> [1] \"Outputs\\nCc\\n\\nStates/Compartments\\ndepot, central\\n\\nCovariates\\nNone found\\n\\nPopulation Parameters\\nlka, lcl, lvc\\n\\nIndividual Parameters\\nka, cl, vc\\n\\nSecondary Parameters\\nkel\\n\\nBetween-Subject Variability\\netalcl\\n\\nResidual Error Parameters\\npropSd\\n\\n\" #>  #> $CTS$elements$element_1$rx_details$ht_info #> <b>Outputs<\/b> #> <br/> #> Cc #> <br/> #> <br/> #> <b>States/Compartments<\/b> #> <br/> #> depot, central #> <br/> #> <br/> #> <b>Covariates<\/b> #> <br/> #> <em>None found<\/em> #> <br/> #> <br/> #> <b>Population Parameters<\/b> #> <br/> #> lka, lcl, lvc #> <br/> #> <br/> #> <b>Individual Parameters<\/b> #> <br/> #> ka, cl, vc #> <br/> #> <br/> #> <b>Secondary Parameters<\/b> #> <br/> #> kel #> <br/> #> <br/> #> <b>Between-Subject Variability<\/b> #> <br/> #> etalcl #> <br/> #> <br/> #> <b>Residual Error Parameters<\/b> #> <br/> #> propSd #> <br/> #> <br/> #>  #>  #> $CTS$elements$element_1$components_table #> NULL #>  #> $CTS$elements$element_1$components_list #> list() #>  #> $CTS$elements$element_1$covariates #> list() #>  #> $CTS$elements$element_1$checksum #> [1] \"309d9f0f0e7937e139ada00751bc8f2a\" #>  #> $CTS$elements$element_1$code #> NULL #>  #> $CTS$elements$element_1$code_ele_only #> NULL #>  #>  #>  #> $CTS$current_element #> [1] \"element_1\" #>  #> $CTS$checksum #> [1] \"4affbc8f5039c017fb6d555c8aa0f450\" #>  #>  #> $MOD_TYPE #> [1] \"CTS\" #>  #> $id #> [1] \"CTS\" #>  #> $dep_mod_ids #> [1] \"MB\" #>  #> $FM_yaml_file #> [1] \"/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/formods/templates/formods.yaml\" #>  #> $MOD_yaml_file #> [1] \"/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/Rtmpke0KwN/temp_libpath790c4fc3c99d/ruminate/templates/CTS.yaml\" #>  #> $shiny_token #> [1] \"non_shiny\" #>"},{"path":"/reference/CTS_new_element.html","id":null,"dir":"Reference","previous_headings":"","what":"New ===Module_Name=== cohort — CTS_new_element","title":"New ===Module_Name=== cohort — CTS_new_element","text":"Appends new empty cohort CTS state object makes new cohort active cohort.","code":""},{"path":"/reference/CTS_new_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"New ===Module_Name=== cohort — CTS_new_element","text":"","code":"CTS_new_element(state)"},{"path":"/reference/CTS_new_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"New ===Module_Name=== cohort — CTS_new_element","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_new_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"New ===Module_Name=== cohort — CTS_new_element","text":"CTS state object containing new cohort cohort set current active cohort. See help CTS_fetch_state() ===ELEMENT== format.","code":""},{"path":"/reference/CTS_new_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"New ===Module_Name=== cohort — CTS_new_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: c8c9f2770a80b6e684999ad3280cf89f #> → CTS: module checksum updated: c5408dd76d32b14bec6868a873035dc1 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 5ccbfa31ae9f099494aca239222cee8a #> → CTS: module checksum updated: abb64ec2c533d6039bc04f28cb32c258"},{"path":"/reference/CTS_plot_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the Specified Element — CTS_plot_element","title":"Plots the Specified Element — CTS_plot_element","text":"Takes CTS state element simulates current set rules.","code":""},{"path":"/reference/CTS_plot_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the Specified Element — CTS_plot_element","text":"","code":"CTS_plot_element(state, element)"},{"path":"/reference/CTS_plot_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the Specified Element — CTS_plot_element","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_plot_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the Specified Element — CTS_plot_element","text":"Simulation element plot results stored '\"plotres\" element. isgood   Boolean value indicating state figure generation code. msgs     messages passed user. capture  Captured figure generation output plot_sr_tc()","code":""},{"path":"/reference/CTS_plot_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots the Specified Element — CTS_plot_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 507e79fa161ff67a9609e14b7de357ca #> → CTS: module checksum updated: 7c6f883475f806f068bf644b95c4cda3 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: e317aba2dfac4ddfcc44570b6ab050f2 #> → CTS: module checksum updated: a2bccd0da1e7904d720bbf938b1b306a"},{"path":"/reference/CTS_set_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the Value for the  Current cohort — CTS_set_current_element","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"Takes CTS state returns current active cohort","code":""},{"path":"/reference/CTS_set_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"","code":"CTS_set_current_element(state, element)"},{"path":"/reference/CTS_set_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_set_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"CTS state object current cohort set using supplied value.","code":""},{"path":"/reference/CTS_set_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the Value for the  Current cohort — CTS_set_current_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: c9ca684414f97a9252ec0fd21d5fc4f2 #> → CTS: module checksum updated: 19724b2e45135e4868442e2fbdf102d2 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 9addee50488e30c656fd49eec9e684a3 #> → CTS: module checksum updated: 118e8d2e0b8d89848e5fc5814f49df1f"},{"path":"/reference/CTS_simulate_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulates the Specified Element — CTS_simulate_element","title":"Simulates the Specified Element — CTS_simulate_element","text":"Takes CTS state element simulates current set rules.","code":""},{"path":"/reference/CTS_simulate_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulates the Specified Element — CTS_simulate_element","text":"","code":"CTS_simulate_element(state, element)"},{"path":"/reference/CTS_simulate_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulates the Specified Element — CTS_simulate_element","text":"state CTS state CTS_fetch_state() element Element list CTS_fetch_current_element()","code":""},{"path":"/reference/CTS_simulate_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulates the Specified Element — CTS_simulate_element","text":"Simulation element simulation results stored \"simres\" element.","code":""},{"path":"/reference/CTS_simulate_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulates the Specified Element — CTS_simulate_element","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: d2a68da2eefbde667d73bdcd09e029f3 #> → CTS: module checksum updated: f075cf38872f1db04a7f59a90a4e1a8e #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 7b12f27e10b95d7dcce318b887404d22 #> → CTS: module checksum updated: 0e2f7ebea1fdee9388660ad4403d55d1"},{"path":"/reference/CTS_test_mksession.html","id":null,"dir":"Reference","previous_headings":"","what":"Populate Session Data for Module Testing — CTS_test_mksession","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"Populates supplied session variable testing.","code":""},{"path":"/reference/CTS_test_mksession.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"","code":"CTS_test_mksession(session, id = \"CTS\", id_MB = \"MB\", full_session = TRUE)"},{"path":"/reference/CTS_test_mksession.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"session Shiny session variable (app) list (outside app) id ID string corresponds ID used call modules UI elements id_MB ID string corresponds ID used call MB modules full_session Boolean indicate full test session created (default TRUE).","code":""},{"path":"/reference/CTS_test_mksession.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"list following elements isgood: Boolean indicating exit status function. session: value Shiny session variable (app) list (outside app) initialization. input: value shiny input end session initialization. state: App state. rsc: react_state components.","code":""},{"path":"/reference/CTS_test_mksession.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populate Session Data for Module Testing — CTS_test_mksession","text":"","code":"sess_res = CTS_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 1f88f4f54c4af6f681203d22c5334e99 #> → CTS: module checksum updated: 605a0c0c0399c998fc17097517cce64f"},{"path":"/reference/CTS_update_checksum.html","id":null,"dir":"Reference","previous_headings":"","what":"Updates CTS Module Checksum — CTS_update_checksum","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"Takes CTS state updates checksum used trigger downstream updates","code":""},{"path":"/reference/CTS_update_checksum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"","code":"CTS_update_checksum(state)"},{"path":"/reference/CTS_update_checksum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"state CTS state CTS_fetch_state()","code":""},{"path":"/reference/CTS_update_checksum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"CTS state object checksum updated","code":""},{"path":"/reference/CTS_update_checksum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updates CTS Module Checksum — CTS_update_checksum","text":"","code":"# For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: library(formods) if(is_installed(\"rxode2\")){  sess_res = CTS_test_mksession(session=list(), full_session=FALSE) state = sess_res$state session = sess_res$session input   = sess_res$input  # Creates a new empty element state = CTS_new_element(state)  # Delete the current element state = CTS_del_current_element(state)  # Fetch a list of the current element element = CTS_fetch_current_element(state)  # You can modify the element element[[\"element_name\"]] = \"A more descriptive name\"  # Simulating the current element element = CTS_simulate_element(state, element)  # You can now place element back in the state state = CTS_set_current_element(state, element)  # This will extract the code for the current module code = CTS_fetch_code(state) code   # This will update the checksum of the module state state = CTS_update_checksum(state)  #'@example inst/test_apps/CTS_funcs.R } #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → CTS:   dest:   file.path(\"config\",\"report.docx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → CTS:   dest:   file.path(\"config\",\"report.pptx\") #> → CTS: including file #> → CTS:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → CTS:   dest:   file.path(\"config\",\"report.yaml\") #> → CTS: module checksum updated: 4affbc8f5039c017fb6d555c8aa0f450 #> → CTS: State initialized #> → CTS: cohort checksum updated: 7b7f6b1ffa36a534ff4d377fb5317f69 #> → CTS: module checksum updated: 15b4e3d941fb672f0346104f34aa2149 #> → CTS: add rule success #> → CTS: rule added #> → CTS: cohort checksum updated: c4e664d7b349ffd79d757f6c40d0c3f3 #> → CTS: module checksum updated: 63cccedeb8c1f215b5f1bdb3251cf32f #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 1e0c56ce4c763c0b6b6d16301d74ea17 #> → CTS: module checksum updated: b884ffedf741025dae39fc01bb131fc2 #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> → CTS: cohort checksum updated: 8685b6b983648242b5574cb4b2725526 #> → CTS: module checksum updated: e7385a0ee085d14791c6460ada0457e6"},{"path":"/reference/FM_fetch_mdl.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Models from Modules in the App — FM_fetch_mdl","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"Loops specified module ID modules ID specified. ID, attempt made extract models available.","code":""},{"path":"/reference/FM_fetch_mdl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"","code":"FM_fetch_mdl(state, session, ids = NULL)"},{"path":"/reference/FM_fetch_mdl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"state Current module state yaml file read session Shiny session variable ids Vector ID strings modules containing models NULL modules models available.","code":""},{"path":"/reference/FM_fetch_mdl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"list containing current dataset following format: JMH isgood: General logical indicator successfully. hasmdl: Logical indicating least one model found. modules: List module checksums. mdl: Result XX_fetch_mdl, see  vignette(\"making_modules\", package = \"formods\") catalog: Dataframe containing tabular catalog models found. label:  Text label model. object :  Name object contains compiled rxode2 model. MOD_TYPE:  Type 'formods' module model came . id: Source 'formods' Module ID. checksum: Checksum module model came . MDLchecksum: Checksum model. code:  Code generate model.","code":""},{"path":"/reference/FM_fetch_mdl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Models from Modules in the App — FM_fetch_mdl","text":"","code":"# We need a module state and a Shiny session variable # to use this function: id=\"UD\" sess_res = UD_test_mksession(session=list(), id=id) #> Error in UD_test_mksession(session = list(), id = id): could not find function \"UD_test_mksession\" session = sess_res$session #> Error in eval(expr, envir, enclos): object 'sess_res' not found state   = sess_res$state #> Error in eval(expr, envir, enclos): object 'sess_res' not found mdl = FM_fetch_mdl(state, session) #> Error in FM_fetch_app_state(session): object 'session' not found mdl$catalog #> Error in eval(expr, envir, enclos): object 'mdl' not found"},{"path":"/reference/MB_Server.html","id":null,"dir":"Reference","previous_headings":"","what":"Model Builder State Server — MB_Server","title":"Model Builder State Server — MB_Server","text":"Server function Model Builder  Shiny Module","code":""},{"path":"/reference/MB_Server.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Builder State Server — MB_Server","text":"","code":"MB_Server(   id,   id_ASM = \"ASM\",   FM_yaml_file = system.file(package = \"formods\", \"templates\", \"formods.yaml\"),   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\"),   deployed = FALSE,   react_state = NULL )"},{"path":"/reference/MB_Server.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Builder State Server — MB_Server","text":"id ID string corresponds ID used call modules UI elements id_ASM ID string app state managment module used save load app states FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. deployed Boolean variable indicating whether app deployed . react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/MB_Server.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model Builder State Server — MB_Server","text":"MB Server object","code":""},{"path":"/reference/MB_append_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Report Elements — MB_append_report","title":"Append Report Elements — MB_append_report","text":"Appends report elements formods report.","code":""},{"path":"/reference/MB_append_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Report Elements — MB_append_report","text":"","code":"MB_append_report(state, rpt, rpttype, gen_code_only = FALSE)"},{"path":"/reference/MB_append_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Report Elements — MB_append_report","text":"state MB state MB_fetch_state() rpt Report current content report appended function. details structure see documentation FM_generate_report rpttype Type report generate (supported \"xlsx\", \"pptx\", \"docx\"). gen_code_only Boolean value indicating code generated (FALSE).","code":""},{"path":"/reference/MB_append_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Report Elements — MB_append_report","text":"list containing following elements isgood:    Return status function. hasrptele: Boolean indicator module reportable elements. code:      Code generate reporting elements. msgs:      Messages passed back user. rpt:       Report additions passed back user.","code":""},{"path":[]},{"path":"/reference/MB_build_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Code to Generate Model — MB_build_code","title":"Build Code to Generate Model — MB_build_code","text":"Takes function definition rxode object, function object name rxode object name creates code build objects.","code":""},{"path":"/reference/MB_build_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Code to Generate Model — MB_build_code","text":"","code":"MB_build_code(state, session, fcn_def, fcn_obj_name, rx_obj_name)"},{"path":"/reference/MB_build_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Code to Generate Model — MB_build_code","text":"state MB state MB_fetch_state() session Shiny session variable fcn_def Character string containing function definition model fcn_obj_name Object name function create. rx_obj_name Object name rxode2 object create.","code":""},{"path":"/reference/MB_build_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Code to Generate Model — MB_build_code","text":"List following elements model_code Block code create model context larger script. model_code_sa model_code element meant stand alone.","code":""},{"path":"/reference/MB_build_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build Code to Generate Model — MB_build_code","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_del_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Deletes Current model — MB_del_current_element","title":"Deletes Current model — MB_del_current_element","text":"Takes MB state deletes current model. last element, new default added.","code":""},{"path":"/reference/MB_del_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deletes Current model — MB_del_current_element","text":"","code":"MB_del_current_element(state)"},{"path":"/reference/MB_del_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deletes Current model — MB_del_current_element","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_del_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deletes Current model — MB_del_current_element","text":"MB state object current model deleted.","code":""},{"path":"/reference/MB_del_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deletes Current model — MB_del_current_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_catalog.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches List of Available Models — MB_fetch_catalog","title":"Fetches List of Available Models — MB_fetch_catalog","text":"Creates catalog models available system file.","code":""},{"path":"/reference/MB_fetch_catalog.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches List of Available Models — MB_fetch_catalog","text":"","code":"MB_fetch_catalog(state)"},{"path":"/reference/MB_fetch_catalog.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches List of Available Models — MB_fetch_catalog","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_catalog.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches List of Available Models — MB_fetch_catalog","text":"List following attributes: summary: Dataframe summary models catlog sources: information found summary table list form. select_group: List models grouped source. select_plain: Flat list models (ungrouped). select_subtext: Subtext pulldown menus. msgs: Messages passed back user. hasmdl: Boolean value indicating models found. isgood: Boolean variable indicating success failure.","code":""},{"path":"/reference/MB_fetch_catalog.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches List of Available Models — MB_fetch_catalog","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Code — MB_fetch_code","title":"Fetch Module Code — MB_fetch_code","text":"Fetches code generate results seen app","code":""},{"path":"/reference/MB_fetch_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Code — MB_fetch_code","text":"","code":"MB_fetch_code(state)"},{"path":"/reference/MB_fetch_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Code — MB_fetch_code","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Code — MB_fetch_code","text":"Character object vector lines code","code":""},{"path":"/reference/MB_fetch_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Code — MB_fetch_code","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_component.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Selected Current Model Component — MB_fetch_component","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"Fetches selected component provided model.","code":""},{"path":"/reference/MB_fetch_component.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"","code":"MB_fetch_component(state, current_ele, component_id = NULL)"},{"path":"/reference/MB_fetch_component.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"state MB state MB_fetch_state() current_ele MB model element MB_fetch_current_element() component_id numeric component id select (default NULL) return selected ID.","code":""},{"path":"/reference/MB_fetch_component.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"list current component following attributes isgood: Boolean object indicating success. rx_obj: rxode2 object model. fcn_def: Just model function definition. note: Note field components_table model_code: Code generate model. model_code_sa: Stand-alone code generate model msgs:      Messages passed back user.","code":""},{"path":"/reference/MB_fetch_component.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Selected Current Model Component — MB_fetch_component","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Current model — MB_fetch_current_element","title":"Fetches Current model — MB_fetch_current_element","text":"Takes MB state returns current active model object.","code":""},{"path":"/reference/MB_fetch_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Current model — MB_fetch_current_element","text":"","code":"MB_fetch_current_element(state)"},{"path":"/reference/MB_fetch_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Current model — MB_fetch_current_element","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Current model — MB_fetch_current_element","text":"List containing details active data view. structure list structure state$MB$elements output MB_fetch_state().","code":""},{"path":"/reference/MB_fetch_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Current model — MB_fetch_current_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_fetch_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Model Builder Module Datasets — MB_fetch_ds","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"Fetches datasets contained module.","code":""},{"path":"/reference/MB_fetch_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"","code":"MB_fetch_ds(state)"},{"path":"/reference/MB_fetch_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"Character object vector lines code list containing following elements isgood:    Return status function. hasds:     Boolean indicator module datasets msgs:      Messages passed back user. ds:        List datasets. list element name R-object dataset. element following structure: label: Text label dataset MOD_TYPE: Short name type module. id: module ID DS: Dataframe containing actual dataset. DSMETA: Metadata describing DS code: Complete code build dataset. checksum: Module checksum. DSchecksum: Dataset checksum.","code":""},{"path":"/reference/MB_fetch_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Model Builder Module Datasets — MB_fetch_ds","text":"","code":"# We need a module state: sess_res = MB_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> → MB: State initialized state = sess_res$state  ds = MB_fetch_ds(state) #> Error in MB_fetch_ds(state): object 'object_name' not found  ds #> Error in eval(expr, envir, enclos): object 'ds' not found"},{"path":"/reference/MB_fetch_mdl.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Model Builder Module Models — MB_fetch_mdl","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"Fetches models contained module.","code":""},{"path":"/reference/MB_fetch_mdl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"","code":"MB_fetch_mdl(state)"},{"path":"/reference/MB_fetch_mdl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_fetch_mdl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"list containing following elements isgood:    Return status function. hasmdl:    Boolean indicator module models msgs:      Messages passed back user. mdl:       List models. list element name R-object dataset. element following structure: label:      Text label model (e.g. one-compartment model). MOD_TYPE:   Type module. id:         Module ID. rx_obj:     rxode2 object name holds model. fcn_def:    Text define model MDLMETA:    Notes model. code:       Code generate model. checksum:   Module checksum. MDLchecksum: Model checksum.","code":""},{"path":"/reference/MB_fetch_mdl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Model Builder Module Models — MB_fetch_mdl","text":"","code":"# We need a module state: sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 state = sess_res$state  mdls = MB_fetch_mdl(state)  names(mdls) #> [1] \"hasmdl\" \"isgood\" \"msgs\"   \"mdl\""},{"path":"/reference/MB_fetch_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Model Builder State — MB_fetch_state","title":"Fetch Model Builder State — MB_fetch_state","text":"Merges default app options changes made UI","code":""},{"path":"/reference/MB_fetch_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Model Builder State — MB_fetch_state","text":"","code":"MB_fetch_state(   id,   id_ASM,   input,   session,   FM_yaml_file,   MOD_yaml_file,   react_state )"},{"path":"/reference/MB_fetch_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Model Builder State — MB_fetch_state","text":"id Shiny module ID id_ASM ID string app state management module used save load app states input Shiny input variable session Shiny session variable FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/MB_fetch_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Model Builder State — MB_fetch_state","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. MB: isgood: Boolean object indicating file successfully loaded. checksum: MD5 sum contents element can used detect changes state. MOD_TYPE: Character data containing type module \"MB\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/MB_fetch_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Model Builder State — MB_fetch_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        id_ASM          = \"ASM\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)"},{"path":"/reference/MB_init_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize MB Module State — MB_init_state","title":"Initialize MB Module State — MB_init_state","text":"Creates list initialized module state","code":""},{"path":"/reference/MB_init_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize MB Module State — MB_init_state","text":"","code":"MB_init_state(FM_yaml_file, MOD_yaml_file, id, session)"},{"path":"/reference/MB_init_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize MB Module State — MB_init_state","text":"FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id ID string module. session Shiny session variable","code":""},{"path":"/reference/MB_init_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize MB Module State — MB_init_state","text":"list containing empty MB state","code":""},{"path":"/reference/MB_init_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize MB Module State — MB_init_state","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 session = sess_res$session input   = sess_res$input  state = MB_init_state(    FM_yaml_file  = system.file(package = \"formods\",                                \"templates\",                                \"formods.yaml\"),    MOD_yaml_file = system.file(package = \"ruminate\",                                \"templates\",                                \"MB.yaml\"),    id              = \"MB\",    session         = session) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized  state #> $yaml #> $yaml$FM #> $yaml$FM$include #> $yaml$FM$include$files #> $yaml$FM$include$files[[1]] #> $yaml$FM$include$files[[1]]$file #> $yaml$FM$include$files[[1]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.docx\\\")\" #>  #> $yaml$FM$include$files[[1]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.docx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[2]] #> $yaml$FM$include$files[[2]]$file #> $yaml$FM$include$files[[2]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.pptx\\\")\" #>  #> $yaml$FM$include$files[[2]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.pptx\\\")\" #>  #>  #>  #> $yaml$FM$include$files[[3]] #> $yaml$FM$include$files[[3]]$file #> $yaml$FM$include$files[[3]]$file$source #> [1] \"file.path(system.file(package=\\\"onbrand\\\"), \\\"templates\\\", \\\"report.yaml\\\")\" #>  #> $yaml$FM$include$files[[3]]$file$dest #> [1] \"file.path(\\\"config\\\",\\\"report.yaml\\\")\" #>  #>  #>  #>  #>  #> $yaml$FM$deployed #> [1] FALSE #>  #> $yaml$FM$code #> $yaml$FM$code$theme #> [1] \"vibrant_ink\" #>  #> $yaml$FM$code$showLineNumbers #> [1] TRUE #>  #> $yaml$FM$code$gen_file #> [1] \"run_analysis.R\" #>  #> $yaml$FM$code$gen_preamble #> [1] \"# formods automated output ------------------------------------------------\\n# https://formods.ubiquity.tools/\\nrm(list=ls())\" #>  #> $yaml$FM$code$packages #> [1] \"onbrand\" \"writexl\" #>  #>  #> $yaml$FM$notifications #> $yaml$FM$notifications$config #> $yaml$FM$notifications$config$success #> $yaml$FM$notifications$config$success$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$success$background #> [1] \"#5bb85b\" #>  #>  #> $yaml$FM$notifications$config$failure #> $yaml$FM$notifications$config$failure$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$failure$background #> [1] \"#d9534f\" #>  #>  #> $yaml$FM$notifications$config$info #> $yaml$FM$notifications$config$info$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$info$background #> [1] \"#5bc0de\" #>  #>  #> $yaml$FM$notifications$config$warning #> $yaml$FM$notifications$config$warning$useFontAwesome #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$useIcon #> [1] FALSE #>  #> $yaml$FM$notifications$config$warning$background #> [1] \"#f0ac4d\" #>  #>  #>  #>  #> $yaml$FM$reporting #> $yaml$FM$reporting$enabled #> [1] TRUE #>  #> $yaml$FM$reporting$content_init #> $yaml$FM$reporting$content_init$xlsx #> [1] \"rpt = list(summary = NULL,\\n           sheets  = list())\" #>  #> $yaml$FM$reporting$content_init$docx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.docx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #> $yaml$FM$reporting$content_init$pptx #> [1] \"rpt  = onbrand::read_template(\\n  template = file.path(\\\"config\\\", \\\"report.pptx\\\"),\\n  mapping  = file.path(\\\"config\\\", \\\"report.yaml\\\"))\" #>  #>  #>  #> $yaml$FM$ui #> $yaml$FM$ui$button_style #> [1] \"fill\" #>  #> $yaml$FM$ui$select_size #> [1] 10 #>  #> $yaml$FM$ui$color_green #> [1] \"#00BB8A\" #>  #> $yaml$FM$ui$color_red #> [1] \"#FF475E\" #>  #> $yaml$FM$ui$color_blue #> [1] \"#0088FF\" #>  #> $yaml$FM$ui$color_purple #> [1] \"#bd2cf4\" #>  #>  #> $yaml$FM$data_meta #> $yaml$FM$data_meta$data_header #> [1] \"<span style='color:===COLOR==='><b>===NAME===<\/b><br/><font size='-3'>===LABEL===<\/font><\/span>\" #>  #> $yaml$FM$data_meta$subtext #> [1] \"===LABEL===: ===RANGE===\" #>  #> $yaml$FM$data_meta$many_sep #> [1] \",⋅⋅⋅,\" #>  #> $yaml$FM$data_meta$data_types #> $yaml$FM$data_meta$data_types$character #> $yaml$FM$data_meta$data_types$character$color #> [1] \"#DD4B39\" #>  #> $yaml$FM$data_meta$data_types$character$label #> [1] \"text\" #>  #>  #> $yaml$FM$data_meta$data_types$double #> $yaml$FM$data_meta$data_types$double$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$double$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$integer #> $yaml$FM$data_meta$data_types$integer$color #> [1] \"#3C8DBC\" #>  #> $yaml$FM$data_meta$data_types$integer$label #> [1] \"num\" #>  #>  #> $yaml$FM$data_meta$data_types$other #> $yaml$FM$data_meta$data_types$other$color #> [1] \"black\" #>  #> $yaml$FM$data_meta$data_types$other$label #> [1] \"other\" #>  #>  #>  #>  #> $yaml$FM$labels #> $yaml$FM$labels$default_ds #> [1] \"Original data set\" #>  #>  #> $yaml$FM$user_files #> $yaml$FM$user_files$use_tmpdir #> [1] TRUE #>  #>  #> $yaml$FM$logging #> $yaml$FM$logging$enabled #> [1] TRUE #>  #> $yaml$FM$logging$timestamp #> [1] TRUE #>  #> $yaml$FM$logging$timestamp_fmt #> [1] \"%Y-%m-%d %H:%M:%S\" #>  #> $yaml$FM$logging$log_file #> [1] \"formods_log.txt\" #>  #> $yaml$FM$logging$console #> [1] TRUE #>  #>  #>  #>  #> $MC #> $MC$element_object_name #> [1] \"MB_obj\" #>  #> $MC$sources #> $MC$sources[[1]] #> $MC$sources[[1]]$source #> $MC$sources[[1]]$source$group #> [1] \"User-defined Models\" #>  #> $MC$sources[[1]]$source$name #> [1] \"rxode2 User model\" #>  #> $MC$sources[[1]]$source$description #> [1] \"User-defined rxode2 model\" #>  #> $MC$sources[[1]]$source$type #> [1] \"rxode2\" #>  #> $MC$sources[[1]]$source$obj #> [1] \"my_fcn\" #>  #> $MC$sources[[1]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.R\\\")\" #>  #>  #>  #> $MC$sources[[2]] #> $MC$sources[[2]]$source #> $MC$sources[[2]]$source$group #> [1] \"User-defined Models\" #>  #> $MC$sources[[2]]$source$name #> [1] \"NONMEM User model\" #>  #> $MC$sources[[2]]$source$description #> [1] \"User-defined NONMEM model\" #>  #> $MC$sources[[2]]$source$type #> [1] \"NONMEM\" #>  #> $MC$sources[[2]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.ctl\\\")\" #>  #>  #>  #> $MC$sources[[3]] #> $MC$sources[[3]]$source #> $MC$sources[[3]]$source$group #> [1] \"Model Library\" #>  #> $MC$sources[[3]]$source$type #> [1] \"nlmixr2lib\" #>  #> $MC$sources[[3]]$source$name #> [1] \"nlmixr2 Model Library\" #>  #>  #>  #>  #> $MC$code #> $MC$code$packages #> [1] \"rxode2\"     \"nonmem2rx\"  \"nlmixr2lib\" #>  #> $MC$code$readOnly #> [1] TRUE #>  #> $MC$code$mode #> [1] \"r\" #>  #>  #> $MC$compact #> $MC$compact$code #> [1] TRUE #>  #> $MC$compact$clip #> [1] TRUE #>  #>  #> $MC$reporting #> $MC$reporting$enabled #> [1] FALSE #>  #> $MC$reporting$priority #> [1] 1 #>  #>  #> $MC$formatting #> $MC$formatting$code #> $MC$formatting$code$width #> [1] 800 #>  #> $MC$formatting$code$height #> [1] 300 #>  #>  #> $MC$formatting$preview #> $MC$formatting$preview$width #> [1] \"800px\" #>  #> $MC$formatting$preview$height #> [1] \"500px\" #>  #>  #> $MC$formatting$input #> NULL #>  #> $MC$formatting$current_element #> $MC$formatting$current_element$width #> [1] \"200px\" #>  #> $MC$formatting$current_element$tooltip #> [1] \"Change the current model.\" #>  #> $MC$formatting$current_element$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$element_name #> $MC$formatting$element_name$width #> [1] \"200px\" #>  #>  #> $MC$formatting$button_clk_run #> $MC$formatting$button_clk_run$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_run$block #> [1] TRUE #>  #>  #> $MC$formatting$button_clk_del #> $MC$formatting$button_clk_del$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_del$block #> [1] TRUE #>  #> $MC$formatting$button_clk_del$tooltip #> [1] \"Delete the current model.\" #>  #> $MC$formatting$button_clk_del$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_save #> $MC$formatting$button_clk_save$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_save$block #> [1] TRUE #>  #> $MC$formatting$button_clk_save$tooltip #> [1] \"Save the name for the current model.\" #>  #> $MC$formatting$button_clk_save$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_clip #> $MC$formatting$button_clk_clip$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_clip$block #> [1] TRUE #>  #> $MC$formatting$button_clk_clip$tooltip #> [1] \"Copy the code to generate the current model to the clipboard.\" #>  #> $MC$formatting$button_clk_clip$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_copy #> $MC$formatting$button_clk_copy$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_copy$block #> [1] TRUE #>  #> $MC$formatting$button_clk_copy$tooltip #> [1] \"Make a copy of the current model.\" #>  #> $MC$formatting$button_clk_copy$tooltip_position #> [1] \"right\" #>  #>  #> $MC$formatting$button_clk_new #> $MC$formatting$button_clk_new$size #> [1] \"sm\" #>  #> $MC$formatting$button_clk_new$block #> [1] TRUE #>  #> $MC$formatting$button_clk_new$tooltip #> [1] \"Create a new model.\" #>  #> $MC$formatting$button_clk_new$tooltip_position #> [1] \"right\" #>  #> $MC$formatting$button_clk_new$width #> [1] 100 #>  #>  #> $MC$formatting$catalog_selection #> $MC$formatting$catalog_selection$width #> [1] \"200px\" #>  #> $MC$formatting$catalog_selection$truncate #> [1] 65 #>  #> $MC$formatting$catalog_selection$tooltip #> [1] \"Select base model from the catalog.\" #>  #> $MC$formatting$catalog_selection$tooltip_position #> [1] \"top\" #>  #>  #> $MC$formatting$model_type_selection #> $MC$formatting$model_type_selection$width #> [1] \"200px\" #>  #> $MC$formatting$model_type_selection$tooltip #> [1] \"Select the type of the model being uploaded.\" #>  #> $MC$formatting$model_type_selection$tooltip_position #> [1] \"right\" #>  #> $MC$formatting$model_type_selection$choices #> $MC$formatting$model_type_selection$choices$rxode2 #> [1] \"rxode2 function\" #>  #> $MC$formatting$model_type_selection$choices$NONMEM #> [1] \"NONMEM (XML, lst, or ctl)\" #>  #>  #> $MC$formatting$model_type_selection$default #> [1] \"rxode2\" #>  #>  #> $MC$formatting$upload_model_file #> $MC$formatting$upload_model_file$width #> [1] \"400px\" #>  #>  #> $MC$formatting$base_from #> $MC$formatting$base_from$size #> [1] \"normal\" #>  #> $MC$formatting$base_from$status #> [1] \"primary\" #>  #> $MC$formatting$base_from$tooltip #> [1] \"You can select the base model from a catalog or upload your own\" #>  #> $MC$formatting$base_from$tooltip_position #> [1] \"bottom\" #>  #> $MC$formatting$base_from$choices #> $MC$formatting$base_from$choices$catalog #> [1] \"Model Catalog\" #>  #> $MC$formatting$base_from$choices$user #> [1] \"User-defined model\" #>  #>  #> $MC$formatting$base_from$default #> [1] \"catalog\" #>  #>  #> $MC$formatting$time_scales #> $MC$formatting$time_scales$width #> [1] \"200px\" #>  #> $MC$formatting$time_scales$default #> [1] \"weeks\" #>  #> $MC$formatting$time_scales$tooltip #> [1] \"Choose the timescale of the model.\" #>  #> $MC$formatting$time_scales$tooltip_position #> [1] \"top\" #>  #> $MC$formatting$time_scales$choices #> $MC$formatting$time_scales$choices$months #> $MC$formatting$time_scales$choices$months$conv #> [1] \"60*60*24*4\" #>  #> $MC$formatting$time_scales$choices$months$verb #> [1] \"Months\" #>  #>  #> $MC$formatting$time_scales$choices$weeks #> $MC$formatting$time_scales$choices$weeks$conv #> [1] \"60*60*24*7\" #>  #> $MC$formatting$time_scales$choices$weeks$verb #> [1] \"Weeks\" #>  #>  #> $MC$formatting$time_scales$choices$days #> $MC$formatting$time_scales$choices$days$conv #> [1] \"60*60*24\" #>  #> $MC$formatting$time_scales$choices$days$verb #> [1] \"Days\" #>  #>  #> $MC$formatting$time_scales$choices$hours #> $MC$formatting$time_scales$choices$hours$conv #> [1] \"60*60\" #>  #> $MC$formatting$time_scales$choices$hours$verb #> [1] \"Hours\" #>  #>  #> $MC$formatting$time_scales$choices$min #> $MC$formatting$time_scales$choices$min$conv #> [1] \"60\" #>  #> $MC$formatting$time_scales$choices$min$verb #> [1] \"Minutes\" #>  #>  #> $MC$formatting$time_scales$choices$sec #> $MC$formatting$time_scales$choices$sec$conv #> [1] \"1\" #>  #> $MC$formatting$time_scales$choices$sec$verb #> [1] \"Seconds\" #>  #>  #>  #>  #>  #> $MC$labels #> $MC$labels$current_element #> NULL #>  #> $MC$labels$element_name #> NULL #>  #> $MC$labels$upload_model_file #> NULL #>  #> $MC$labels$base_from #> NULL #>  #> $MC$labels$catalog_selection #> NULL #>  #> $MC$labels$model_type_selection #> NULL #>  #> $MC$labels$time_scale #> NULL #>  #> $MC$labels$catalog_empty #> [1] \"No models were found, the catalog is empty\" #>  #> $MC$labels$save_btn #> [1] \"Save\" #>  #> $MC$labels$clip_btn #> [1] \"Code\" #>  #> $MC$labels$copy_btn #> [1] \"Copy\" #>  #> $MC$labels$del_btn #> [1] \"Delete\" #>  #> $MC$labels$new_btn #> [1] \"New\" #>  #> $MC$labels$building_model #> [1] \"Building model, be patient.\" #>  #> $MC$labels$element_name_diff #> [1] \"The model name has changed.\" #>  #> $MC$labels$model_code_diff #> [1] \"Manual changes have been made to the model.\" #>  #> $MC$labels$save_change_detected #> [1] \"You need to click on the save button for these changes to take effect.\" #>  #> $MC$labels$head_base_model #> [1] \"Starting model\" #>  #> $MC$labels$head_model_code #> [1] \"Model code\" #>  #> $MC$labels$head_time_scale #> [1] \"Model time-scale\" #>  #>  #> $MC$errors #> $MC$errors$no_model_found #> [1] \"No model was found.\" #>  #> $MC$errors$base_model_build_failed #> [1] \"The base model build failed.\" #>  #> $MC$errors$manual_model_update_failed #> [1] \"Manual model update failed.\" #>  #> $MC$errors$user_file_upload_failed #> [1] \"User-defined model failed.\" #>  #> $MC$errors$nlmixr2lib_not_found #> [1] \"The nlmixr2lib package was not found. This library will not be available.\" #>  #> $MC$errors$selected_id_bad_list #> [1] \"Unable to find a list for the selected component.\" #>  #> $MC$errors$selected_id_bad_row #> [1] \"Selected component should have 1 row.\" #>  #>  #> $MC$tooltips #> $MC$tooltips$include #> [1] TRUE #>  #> $MC$tooltips$elements #> [1] \"Show model components\" #>  #> $MC$tooltips$show_code #> [1] \"Show model code\" #>  #> $MC$tooltips$url_rxode #> [1] \"https://nlmixr2.github.io/rxode2/articles/rxode2-syntax.html\" #>  #> $MC$tooltips$url_model_types #> [1] \"https://ruminate.ubiquity.tools/articles/model_builder.html#model-catalog\" #>  #> $MC$tooltips$components #> $MC$tooltips$components$input_tip #> [1] \"My tool tip\" #>  #>  #>  #>  #> $MB #> $MB$button_counters #> $MB$button_counters$button_clk_save #> [1] 0 #>  #> $MB$button_counters$button_clk_clip #> [1] 0 #>  #> $MB$button_counters$button_clk_del #> [1] 0 #>  #> $MB$button_counters$button_clk_copy #> [1] 0 #>  #> $MB$button_counters$button_clk_new #> [1] 0 #>  #>  #> $MB$ui_hold #> $MB$ui_hold$button_clk_save #> [1] FALSE #>  #> $MB$ui_hold$button_clk_clip #> [1] FALSE #>  #> $MB$ui_hold$button_clk_del #> [1] FALSE #>  #> $MB$ui_hold$button_clk_copy #> [1] FALSE #>  #> $MB$ui_hold$button_clk_new #> [1] FALSE #>  #> $MB$ui_hold$catalog_selection #> [1] FALSE #>  #> $MB$ui_hold$base_from #> [1] FALSE #>  #> $MB$ui_hold$element_name #> [1] FALSE #>  #> $MB$ui_hold$time_scale #> [1] FALSE #>  #> $MB$ui_hold$model_type_selection #> [1] FALSE #>  #> $MB$ui_hold$ui_select_element #> [1] FALSE #>  #> $MB$ui_hold$ui_mb_model #> [1] FALSE #>  #> $MB$ui_hold$uploaded_model #> [1] FALSE #>  #> $MB$ui_hold$element_selection #> [1] FALSE #>  #>  #> $MB$ui_ids #>  [1] \"button_clk_save\"      \"button_clk_clip\"      \"button_clk_del\"       #>  [4] \"button_clk_copy\"      \"button_clk_new\"       \"catalog_selection\"    #>  [7] \"base_from\"            \"element_name\"         \"time_scale\"           #> [10] \"model_type_selection\" \"ui_select_element\"    \"ui_mb_model\"          #> [13] \"model_type_selection\" \"uploaded_model\"       \"element_selection\"    #>  #> $MB$isgood #> [1] TRUE #>  #> $MB$ui_ele #> [1] \"catalog_selection\" \"base_from\"         \"element_name\"      #> [4] \"time_scale\"        #>  #> $MB$element_cntr #> [1] 1 #>  #> $MB$model_type_selection #> [1] \"rxode2\" #>  #> $MB$suggested #> $MB$suggested$found #> [1] TRUE #>  #> $MB$suggested$pkgs #> $MB$suggested$pkgs$rxode2 #> $MB$suggested$pkgs$rxode2$found #> [1] TRUE #>  #> $MB$suggested$pkgs$rxode2$msg #> [1] \"\" #>  #>  #> $MB$suggested$pkgs$nonmem2rx #> $MB$suggested$pkgs$nonmem2rx$found #> [1] TRUE #>  #> $MB$suggested$pkgs$nonmem2rx$msg #> [1] \"\" #>  #>  #> $MB$suggested$pkgs$nlmixr2lib #> $MB$suggested$pkgs$nlmixr2lib$found #> [1] TRUE #>  #> $MB$suggested$pkgs$nlmixr2lib$msg #> [1] \"\" #>  #>  #>  #>  #> $MB$model_catalog #> $MB$model_catalog$sources #> $MB$model_catalog$sources[[1]] #> $MB$model_catalog$sources[[1]]$source #> $MB$model_catalog$sources[[1]]$source$group #> [1] \"User-defined Models\" #>  #> $MB$model_catalog$sources[[1]]$source$name #> [1] \"rxode2 User model\" #>  #> $MB$model_catalog$sources[[1]]$source$description #> [1] \"User-defined rxode2 model\" #>  #> $MB$model_catalog$sources[[1]]$source$type #> [1] \"rxode2\" #>  #> $MB$model_catalog$sources[[1]]$source$obj #> [1] \"my_fcn\" #>  #> $MB$model_catalog$sources[[1]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.R\\\")\" #>  #>  #>  #> $MB$model_catalog$sources[[2]] #> $MB$model_catalog$sources[[2]]$source #> $MB$model_catalog$sources[[2]]$source$group #> [1] \"User-defined Models\" #>  #> $MB$model_catalog$sources[[2]]$source$name #> [1] \"NONMEM User model\" #>  #> $MB$model_catalog$sources[[2]]$source$description #> [1] \"User-defined NONMEM model\" #>  #> $MB$model_catalog$sources[[2]]$source$type #> [1] \"NONMEM\" #>  #> $MB$model_catalog$sources[[2]]$source$file #> [1] \"file.path(getwd(),\\\"user_model.ctl\\\")\" #>  #>  #>  #> $MB$model_catalog$sources[[3]] #> $MB$model_catalog$sources[[3]]$source #> $MB$model_catalog$sources[[3]]$source$group #> [1] \"Model Library\" #>  #> $MB$model_catalog$sources[[3]]$source$type #> [1] \"nlmixr2lib\" #>  #> $MB$model_catalog$sources[[3]]$source$name #> [1] \"nlmixr2 Model Library\" #>  #>  #>  #>  #> $MB$model_catalog$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models #>  #> $MB$model_catalog$select_group #> $MB$model_catalog$select_group$`Model Library` #> $MB$model_catalog$select_group$`Model Library`$PK_1cmt #> [1] \"mod_1\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_1cmt_des #> [1] \"mod_2\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt #> [1] \"mod_3\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_des #> [1] \"mod_4\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_no_depot #> [1] \"mod_5\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_tdcl_des #> [1] \"mod_6\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_3cmt #> [1] \"mod_7\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_3cmt_des #> [1] \"mod_8\" #>  #> $MB$model_catalog$select_group$`Model Library`$indirect_0cpt_transitEx #> [1] \"mod_9\" #>  #> $MB$model_catalog$select_group$`Model Library`$PK_2cmt_mAb_Davda_2014 #> [1] \"mod_10\" #>  #> $MB$model_catalog$select_group$`Model Library`$CarlssonPetri_2021_liraglutide #> [1] \"mod_11\" #>  #> $MB$model_catalog$select_group$`Model Library`$Kovalenko_2020_dupilumab #> [1] \"mod_12\" #>  #> $MB$model_catalog$select_group$`Model Library`$Soehoel_2022_tralokinumab #> [1] \"mod_13\" #>  #> $MB$model_catalog$select_group$`Model Library`$Zhu_2017_lebrikizumab #> [1] \"mod_14\" #>  #> $MB$model_catalog$select_group$`Model Library`$oncology_sdm_lobo_2002 #> [1] \"mod_15\" #>  #> $MB$model_catalog$select_group$`Model Library`$oncology_xenograft_simeoni_2004 #> [1] \"mod_16\" #>  #>  #>  #> $MB$model_catalog$select_plain #> $MB$model_catalog$select_plain$PK_1cmt #> [1] \"mod_1\" #>  #> $MB$model_catalog$select_plain$PK_1cmt_des #> [1] \"mod_2\" #>  #> $MB$model_catalog$select_plain$PK_2cmt #> [1] \"mod_3\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_des #> [1] \"mod_4\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_no_depot #> [1] \"mod_5\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_tdcl_des #> [1] \"mod_6\" #>  #> $MB$model_catalog$select_plain$PK_3cmt #> [1] \"mod_7\" #>  #> $MB$model_catalog$select_plain$PK_3cmt_des #> [1] \"mod_8\" #>  #> $MB$model_catalog$select_plain$indirect_0cpt_transitEx #> [1] \"mod_9\" #>  #> $MB$model_catalog$select_plain$PK_2cmt_mAb_Davda_2014 #> [1] \"mod_10\" #>  #> $MB$model_catalog$select_plain$CarlssonPetri_2021_liraglutide #> [1] \"mod_11\" #>  #> $MB$model_catalog$select_plain$Kovalenko_2020_dupilumab #> [1] \"mod_12\" #>  #> $MB$model_catalog$select_plain$Soehoel_2022_tralokinumab #> [1] \"mod_13\" #>  #> $MB$model_catalog$select_plain$Zhu_2017_lebrikizumab #> [1] \"mod_14\" #>  #> $MB$model_catalog$select_plain$oncology_sdm_lobo_2002 #> [1] \"mod_15\" #>  #> $MB$model_catalog$select_plain$oncology_xenograft_simeoni_2004 #> [1] \"mod_16\" #>  #>  #> $MB$model_catalog$select_subtext #>  [1] \"One compartment PK model with linear clearance\"                                                                           #>  [2] \"One compartment PK model with linear clearance using differential equations\"                                              #>  [3] \"Two compartment PK model with linear clearance\"                                                                           #>  [4] \"Two compartment PK model with linear clearance using differential equations\"                                              #>  [5] \"Two compartment PK model with linear clearance using differential equations\"                                              #>  [6] \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\" #>  [7] \"Three compartment PK model with linear clearance\"                                                                         #>  [8] \"Three compartment PK model with linear clearance using differential equations\"                                            #>  [9] \"Two compartment PK model with Michealis-Menten clearance using differential equations\"                                    #> [10] \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"                            #> [11] \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"                                                                #> [12] \"Dupilumab PK model (Kovalenko 2020)\"                                                                                      #> [13] \"Tralokinumab PK model (Soehoel 2022)\"                                                                                     #> [14] \"Lebrikizumab PK model (Zhu 2017)\"                                                                                         #> [15] \"Signal transduction model for delayed concentration effects on cancer cell growth\"                                        #> [16] \"Oncology tumor growth model in xenograft models\"                                                                          #>  #> $MB$model_catalog$msgs #> NULL #>  #> $MB$model_catalog$hasmdl #> [1] TRUE #>  #> $MB$model_catalog$isgood #> [1] TRUE #>  #>  #> $MB$elements #> $MB$elements$element_1 #> $MB$elements$element_1$isgood #> [1] TRUE #>  #> $MB$elements$element_1$ui #> $MB$elements$element_1$ui$ui_mb_model #> [1] \"\" #>  #> $MB$elements$element_1$ui$time_scale #> [1] \"weeks\" #>  #> $MB$elements$element_1$ui$element_name #> [1] \"Model 1\" #>  #> $MB$elements$element_1$ui$catalog_selection #> [1] \"mod_1\" #>  #> $MB$elements$element_1$ui$base_from #> [1] \"catalog\" #>  #>  #> $MB$elements$element_1$id #> [1] \"element_1\" #>  #> $MB$elements$element_1$idx #> [1] 1 #>  #> $MB$elements$element_1$fcn_obj_name #> [1] \"MB_obj_1_fcn\" #>  #> $MB$elements$element_1$rx_obj_name #> [1] \"MB_obj_1_rx\" #>  #> $MB$elements$element_1$msgs #> NULL #>  #> $MB$elements$element_1$code_previous #> [1] \"\" #>  #> $MB$elements$element_1$update_model_code #> [1] FALSE #>  #> $MB$elements$element_1$components_table #> data frame with 0 columns and 0 rows #>  #> $MB$elements$element_1$selected_component_id #> NULL #>  #> $MB$elements$element_1$components_list #> list() #>  #> $MB$elements$element_1$checksum #> [1] \"4d23d48e7503c4f405af82cd71527ff3\" #>  #> $MB$elements$element_1$base_model_name #> [1] \"\" #>  #> $MB$elements$element_1$base_model #> [1] \"\" #>  #>  #>  #> $MB$current_element #> [1] \"element_1\" #>  #> $MB$checksum #> [1] \"a8d5d330f91feb7be769e08518458dd8\" #>  #>  #> $MOD_TYPE #> [1] \"MB\" #>  #> $id #> [1] \"MB\" #>  #> $FM_yaml_file #> [1] \"/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/formods/templates/formods.yaml\" #>  #> $MOD_yaml_file #> [1] \"/private/var/folders/l9/6pm7xq5j1hn6l00zrs22j6mc0000gn/T/Rtmpke0KwN/temp_libpath790c4fc3c99d/ruminate/templates/MB.yaml\" #>  #> $shiny_token #> [1] \"non_shiny\" #>"},{"path":"/reference/MB_mod_ok.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes Sure Suggests are Installed — MB_mod_ok","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"Simple check make sure suggested packages installed.","code":""},{"path":"/reference/MB_mod_ok.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"","code":"MB_mod_ok(pkgs = c(\"rxode2\", \"nonmem2rx\", \"nlmixr2lib\"))"},{"path":"/reference/MB_mod_ok.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"pkgs List suggested packages (default \"rxode2\", \"nonmem2rx\" \"nlmixr2lib\")","code":""},{"path":"/reference/MB_mod_ok.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"Boolean value indicating module OK","code":""},{"path":"/reference/MB_mod_ok.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Makes Sure Suggests are Installed — MB_mod_ok","text":"","code":"MB_mod_ok() #> [1] FALSE"},{"path":"/reference/MB_new_element.html","id":null,"dir":"Reference","previous_headings":"","what":"New Model Building Model — MB_new_element","title":"New Model Building Model — MB_new_element","text":"Appends new empty model MB state object makes new model active model.","code":""},{"path":"/reference/MB_new_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"New Model Building Model — MB_new_element","text":"","code":"MB_new_element(state)"},{"path":"/reference/MB_new_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"New Model Building Model — MB_new_element","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_new_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"New Model Building Model — MB_new_element","text":"MB state object containing new model model set current active model. See help MB_fetch_state() model format.","code":""},{"path":"/reference/MB_new_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"New Model Building Model — MB_new_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_set_current_element.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the Value for the  Current model — MB_set_current_element","title":"Sets the Value for the  Current model — MB_set_current_element","text":"Takes MB state returns current active model","code":""},{"path":"/reference/MB_set_current_element.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the Value for the  Current model — MB_set_current_element","text":"","code":"MB_set_current_element(state, element)"},{"path":"/reference/MB_set_current_element.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the Value for the  Current model — MB_set_current_element","text":"state MB state MB_fetch_state() element Element list MB_fetch_current_element()","code":""},{"path":"/reference/MB_set_current_element.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the Value for the  Current model — MB_set_current_element","text":"MB state object current model set using supplied value.","code":""},{"path":"/reference/MB_set_current_element.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the Value for the  Current model — MB_set_current_element","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_test_catalog.html","id":null,"dir":"Reference","previous_headings":"","what":"Tests the Model Catalog — MB_test_catalog","title":"Tests the Model Catalog — MB_test_catalog","text":"Reads models catalog attempts build .","code":""},{"path":"/reference/MB_test_catalog.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tests the Model Catalog — MB_test_catalog","text":"","code":"MB_test_catalog(state, as_cran = FALSE, verbose = TRUE)"},{"path":"/reference/MB_test_catalog.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tests the Model Catalog — MB_test_catalog","text":"state MB state MB_fetch_state() as_cran Boolean indicate running CRAN verbose Boolean indicate messages displayed.","code":""},{"path":"/reference/MB_test_catalog.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tests the Model Catalog — MB_test_catalog","text":"List following attributes: isgood: Boolean varaible indicating models catalog passed test. msgs: Messages indicating test successful .","code":""},{"path":"/reference/MB_test_catalog.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tests the Model Catalog — MB_test_catalog","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/MB_test_mksession.html","id":null,"dir":"Reference","previous_headings":"","what":"Populate Session Data for Module Testing — MB_test_mksession","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"Populates supplied session variable testing.","code":""},{"path":"/reference/MB_test_mksession.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"","code":"MB_test_mksession(session, id = \"MB\", full_session = TRUE)"},{"path":"/reference/MB_test_mksession.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"session Shiny session variable (app) list (outside app) id ID string corresponds ID used call modules UI elements full_session Boolean indicate full test session created (default TRUE).","code":""},{"path":"/reference/MB_test_mksession.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"list following elements isgood: Boolean indicating exit status function. session: value Shiny session variable (app) list (outside app) initialization. input: value shiny input end session initialization. state: App state. rsc: react_state components.","code":""},{"path":"/reference/MB_test_mksession.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populate Session Data for Module Testing — MB_test_mksession","text":"","code":"sess_res = MB_test_mksession(session=list(), full_session=FALSE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905"},{"path":"/reference/MB_update_checksum.html","id":null,"dir":"Reference","previous_headings":"","what":"Update MB Module Checksum — MB_update_checksum","title":"Update MB Module Checksum — MB_update_checksum","text":"Takes MB state updates checksum used trigger downstream updates","code":""},{"path":"/reference/MB_update_checksum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update MB Module Checksum — MB_update_checksum","text":"","code":"MB_update_checksum(state)"},{"path":"/reference/MB_update_checksum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update MB Module Checksum — MB_update_checksum","text":"state MB state MB_fetch_state()","code":""},{"path":"/reference/MB_update_checksum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update MB Module Checksum — MB_update_checksum","text":"MB state object checksum updated","code":""},{"path":"/reference/MB_update_checksum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update MB Module Checksum — MB_update_checksum","text":"","code":"# Within shiny both session and input variables will exist, # this creates examples here for testing purposes: sess_res = MB_test_mksession(session=list()) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 session = sess_res$session input   = sess_res$input  # We also need a state variable state = sess_res$state  state = MB_update_checksum(state)"},{"path":"/reference/MB_update_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Updates Current Element with rxode2 Model — MB_update_model","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"Takes rxode2 object updates model components current element.","code":""},{"path":"/reference/MB_update_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"","code":"MB_update_model(state, session, current_ele, rx_obj, note, reset = FALSE)"},{"path":"/reference/MB_update_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"state MB state MB_fetch_state() session Shiny session variable current_ele MB model element MB_fetch_current_element() rx_obj rxode2 model rxode2::rxode2() note text indicating update (e.g. \"added parameter\") reset boolean indicating element needs reset (.e. change base model) default: FALSE.","code":""},{"path":"/reference/MB_update_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"current_element model attached","code":""},{"path":"/reference/MB_update_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updates Current Element with rxode2 Model — MB_update_model","text":"","code":"#library(ruminate) # This will get the full session: sess_res = MB_test_mksession(session=list(), full_session=TRUE) #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → MB:   dest:   file.path(\"config\",\"report.docx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → MB:   dest:   file.path(\"config\",\"report.pptx\") #> → MB: including file #> → MB:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → MB:   dest:   file.path(\"config\",\"report.yaml\") #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #> → MB: module checksum updated:a8d5d330f91feb7be769e08518458dd8 #> → MB: State initialized #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: 04c4a233276d3bd6cbefe58cce05fbd1 #> → MB: module checksum updated:744fb14ce39ea9d846513e6bc52ee905 #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → MB: model checksum updated: f3dd1904f0f09fe0ed4d13347a620749 #> → MB: module checksum updated:faf5d08a8606e06f198cb8a233bcad72 # This is just for CRAN #sess_res = MB_test_mksession(session=list()) session = sess_res$session input   = sess_res$input  # Configuration files FM_yaml_file  = system.file(package = \"formods\", \"templates\", \"formods.yaml\") MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"MB.yaml\")  # Creating an empty state object state = MB_fetch_state(id              = \"MB\",                        input           = input,                        session         = session,                        FM_yaml_file    = FM_yaml_file,                        MOD_yaml_file   = MOD_yaml_file,                        react_state     = NULL)  # This will provide a list of the available models models = MB_fetch_catalog(state) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' # This is a summary of the tables in the model: models$summary #>    mod_id                            Name                          Object #> 1   mod_1                         PK_1cmt                         PK_1cmt #> 2   mod_2                     PK_1cmt_des                     PK_1cmt_des #> 3   mod_3                         PK_2cmt                         PK_2cmt #> 4   mod_4                     PK_2cmt_des                     PK_2cmt_des #> 5   mod_5                PK_2cmt_no_depot                PK_2cmt_no_depot #> 6   mod_6                PK_2cmt_tdcl_des                PK_2cmt_tdcl_des #> 7   mod_7                         PK_3cmt                         PK_3cmt #> 8   mod_8                     PK_3cmt_des                     PK_3cmt_des #> 9   mod_9         indirect_0cpt_transitEx         indirect_0cpt_transitEx #> 10 mod_10          PK_2cmt_mAb_Davda_2014          PK_2cmt_mAb_Davda_2014 #> 11 mod_11  CarlssonPetri_2021_liraglutide  CarlssonPetri_2021_liraglutide #> 12 mod_12        Kovalenko_2020_dupilumab        Kovalenko_2020_dupilumab #> 13 mod_13       Soehoel_2022_tralokinumab       Soehoel_2022_tralokinumab #> 14 mod_14           Zhu_2017_lebrikizumab           Zhu_2017_lebrikizumab #> 15 mod_15          oncology_sdm_lobo_2002          oncology_sdm_lobo_2002 #> 16 mod_16 oncology_xenograft_simeoni_2004 oncology_xenograft_simeoni_2004 #>      Type #> 1  rxode2 #> 2  rxode2 #> 3  rxode2 #> 4  rxode2 #> 5  rxode2 #> 6  rxode2 #> 7  rxode2 #> 8  rxode2 #> 9  rxode2 #> 10 rxode2 #> 11 rxode2 #> 12 rxode2 #> 13 rxode2 #> 14 rxode2 #> 15 rxode2 #> 16 rxode2 #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Model #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PK_1cmt <- function() {\\n  description <- \"One compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_1cmt_des <- function() {\\n  description <- \"One compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3.45 ; label(\"Central volume of distribution (V)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc  <- exp(lvc)\\n\\n    kel <- cl / vc\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <- ka*depot-kel*central\\n\\n    Cc <- central / vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PK_2cmt <- function() {\\n  description <- \"Two compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    Cc<- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    PK_2cmt_des <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_2cmt_no_depot <- function() {\\n  description <- \"Two compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n    \\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    \\n  \\n    d/dt(central) <-  - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n    \\n    Cc ~ prop(propSd)\\n  })\\n} #> 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               PK_2cmt_tdcl_des <- function() {\\n  description <- \"Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)\"\\n  reference <- \"C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis.  Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656\"\\n  ini({\\n    lcl <- log(0.2) ; label(\"Time-stationary clearance (CLTS)\")\\n    lcltmax <- log(0.22) ; label(\"Typical value of the maximal change of clearance relative to baseline (Tmax)\")\\n    lclgamma <- log(1) ; label(\"Hill coefficient for time-dependent clearance\")\\n    lclt50 <- log(30) ; label(\"Time for 50% of maximal CL change\")\\n    lvc  <- log(20) ; label(\"Central volume of distribution (V)\")\\n    lvp  <- log(150) ; label(\"Peripheral volume of distribution (Vp)\")\\n    lq  <- log(0.75) ; label(\"Intercompartmental clearance (Q)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    clts <- exp(lcl)\\n    cltmax <- exp(lcltmax)\\n    clgamma <- exp(lclgamma)\\n    clt50 <- exp(lclt50)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q  <- exp(lq)\\n\\n    cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n\\n    d/dt(central)     <- - kel*central - k12*central + k21*peripheral1\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        PK_3cmt <- function() {\\n  description <- \"Three compartment PK model with linear clearance\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q   <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           PK_3cmt_des <- function() {\\n  description <- \"Three compartment PK model with linear clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lcl <- 1 ; label(\"Clearance (CL)\")\\n    lvc  <- 3 ; label(\"Central volume of distribution (V)\")\\n    lvp  <- 5 ; label(\"Peripheral volume of distribution (Vp)\")\\n    lvp2  <- 8 ; label(\"Second peripheral volume of distribution (Vp2)\")\\n    lq  <- 0.1 ; label(\"Intercompartmental clearance (Q)\")\\n    lq2  <- 0.5 ; label(\"Second intercompartmental clearance (Q2)\")\\n    propSd <- 0.5 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl <- exp(lcl)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    vp2 <- exp(lvp2)\\n    q  <- exp(lq)\\n    q2  <- exp(lq2)\\n\\n    kel <- cl/vc\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    k13 <- q2/vc\\n    k31 <- q2/vp2\\n\\n    d/dt(depot) <- -ka*depot\\n    d/dt(central) <-  ka*depot - kel*central - k12*central + k21*peripheral1 - k13*central + k31*peripheral2\\n    d/dt(peripheral1) <- k12*central - k21*peripheral1\\n    d/dt(peripheral2) <- k13*central - k31*peripheral2\\n    Cc <- central / vc\\n\\n    Cc ~ prop(propSd)\\n  })\\n} #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     indirect_0cpt_transitEx <- function() {\\n  description <- \"Two compartment PK model with Michealis-Menten clearance using differential equations\"\\n  ini({\\n    lka <- 0.45 ; label(\"Absorption rate (Ka)\")\\n    lktr1 <- c(0, 0.05)\\n    lktr2 <- c(0, 0.05)\\n    lktr3 <- c(0, 0.05)\\n    lktr4 <- c(0, 0.05)\\n    lktr5 <- c(0, 0.05)\\n    lktr6 <- c(0, 0.05)\\n    lktr7 <- c(0, 0.05)\\n    lktr8 <- c(0, 0.05)\\n    lktr9 <- c(0, 0.05)\\n    lktr10 <- c(0, 0.05)\\n    lvm <- 0.04\\n    label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    lkm <- 0.01\\n    label(\"Michaelis-Menten constant (mg/L)\")\\n    lvc <- 3\\n    label(\"central volume of distribution (Vc)\")\\n    lvp <- 5\\n    label(\"Peripheral volume of distribution (Vp)\")\\n    lq <- 0.1\\n    label(\"Intercompartmental clearance (Q)\")\\n    propSd <- c(0, 0.5)\\n    label(\"Proportional residual error (fraction)\")\\n\\n  })\\n  model({\\n    ka <- exp(lka)\\n    ktr1 <- exp(lktr1)\\n    ktr2 <- exp(lktr2)\\n    ktr3 <- exp(lktr3)\\n    ktr4 <- exp(lktr4)\\n    ktr5 <- exp(lktr5)\\n    ktr6 <- exp(lktr6)\\n    ktr7 <- exp(lktr7)\\n    ktr8 <- exp(lktr8)\\n    ktr9 <- exp(lktr9)\\n    ktr10 <- exp(lktr10)\\n    km <- exp(lkm)\\n    vm <- exp(lvm)\\n    vc <- exp(lvc)\\n    vp <- exp(lvp)\\n    q <- exp(lq)\\n    k12 <- q/vc\\n    k21 <- q/vp\\n    d/dt(depot) <- -ka*depot\\n    d/dt(transit1) <- ka * depot - ktr1 * transit1\\n    d/dt(transit2) <- ktr1 * transit1 - ktr2 * transit2\\n    d/dt(transit3) <- ktr2 * transit2 - ktr3 * transit3\\n    d/dt(transit4) <- ktr3 * transit3 - ktr4 * transit4\\n    d/dt(transit5) <- ktr4 * transit4 - ktr5 * transit5\\n    d/dt(transit6) <- ktr5 * transit5 - ktr6 * transit6\\n    d/dt(transit7) <- ktr6 * transit6 - ktr7 * transit7\\n    d/dt(transit8) <- ktr7 * transit7 - ktr8 * transit8\\n    d/dt(transit9) <- ktr8 * transit8 - ktr9 * transit9\\n    d/dt(transit10) <- ktr9 * transit9 - ktr10 * transit10\\n    d/dt(central) <- ktr10 * transit10 - (vm/(km + central/vc)) * \\n      central - k12 * central + k21 * peripheral1\\n    d/dt(peripheral1) <- k12 * central - k21 * peripheral1\\n    Cc <- central/vc\\n    Cc ~ prop(propSd)\\n  })\\n} #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   PK_2cmt_mAb_Davda_2014 <- function() {\\n  description <- \"Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014)\"\\n  reference <- \"Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs JP. A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. MAbs. 2014;6(4):1094-1102. doi:10.4161/mabs.29095\"\\n  ini({\\n    lfdepot <- log(0.744) ; label(\"Subcutaneous bioavailability (fraction)\")\\n    lka <- log(0.282) ; label(\"Absorption rate (Ka, 1/day)\")\\n    lcl <- log(0.200) ; label(\"Clearance (CL, L/day)\")\\n    lv  <- log(3.61) ; label(\"Central volume of distribution (V, L)\")\\n    lvp  <- log(2.75) ; label(\"Peripheral volume of distribution (Vp, L)\")\\n    lq  <- log(0.747) ; label(\"Intercompartmental clearance (Q, L/day)\")\\n\\n    allocl <- 0.865 ; label(\"Allometric exponent on clearance and intercompartmental clearance (unitless)\")\\n    allov <- 0.957 ; label(\"Allometric exponent on volumes of distribution (unitless)\")\\n\\n    etafdepot ~ 0\\n    etaka ~ 0.416\\n    etacl + etav + etavp ~ c(0.0987,\\n                             0.0786, 0.116,\\n                             0.0377, 0.0619, 0.0789)\\n    etaq ~ 0.699\\n\\n    prop.err <- 0.144 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    # WT is body weight in kg\\n    fdepot <- exp(lfdepot + etafdepot)\\n    ka <- exp(lka + etaka)\\n    wtnorm <- log(WT/70)\\n    cl <- exp(lcl + allocl*wtnorm + etacl)\\n    q  <- exp(lq + allocl*wtnorm + etaq)\\n    v <- exp(lv + allov*wtnorm + etav)\\n    vp <- exp(lvp + allov*wtnorm + etavp)\\n    Cc <- linCmt()\\n    \\n    f(depot) <- fdepot  # Units are dosing units/L (typically mg/L = ug/mL)\\n    Cc ~ prop(prop.err)\\n    \\n  })\\n} #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              CarlssonPetri_2021_liraglutide <- function() {\\n  description <- \"Liraglutide PK model in adolescents (Carlsson Petri 2021)\"\\n  reference <- \"Carlsson Petri KC, Hale PM, Hesse D, Rathor N, Mastrandrea LD. Liraglutide pharmacokinetics and exposure-response in adolescents with obesity. Pediatric Obesity. 2021;16(10):e12799. doi:10.1111/ijpo.12799\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      CHILD = \"Is the subject a child? (1 for yes, 0 for no)\",\\n      ADOLESCENT = \"Is the subject an adolescent? (1 for yes, 0 for no)\",\\n      SEXM = \"1 for male, 0 for female\"\\n    )\\n  ini({\\n    lka <- fixed(log(0.0813)) ; label(\"Absorption rate (1/hr)\")\\n    lcl <- log(1.01) ; label(\"Apparent clearance (L/h)\")\\n    e_wt_cl <- 0.762; label(\"Body weight exponent on CL/F\")\\n    e_sex_cl <- 1.12; label(\"Sex contrast (male/female) on CL/F\")\\n    e_age_child_cl <- 1.11; label(\"Age contrast (child/adult) on CL/F\")\\n    e_age_adolescent_cl <- 1.06; label(\"Age contrast (adolescent/adult) on CL/F\")\\n    lvc  <- fixed(log(13.8)) ; label(\"Apparent central volume of distribution (L)\")\\n    e_wt_vc <- 0.587; label(\"Body weight exponent on Vc/F\")\\n    \\n    etalcl ~ log(1.312)\\n    etalvc ~ log(1.317)\\n    propSd <- 0.433 ; label(\"Proportional residual error (fraction)\")\\n  })\\n  model({\\n    ka <- exp(lka)\\n    cl_wt <- (WT/100)^e_wt_cl # Equation 2 in the paper\\n    cl_sex <- SEXM^e_sex_cl # Equation 3 in the paper\\n    cl_age <- CHILD^e_age_child_cl * ADOLESCENT^e_age_adolescent_cl # Equation 4 in the paper\\n    cl <- exp(lcl + etalcl)*cl_wt*cl_sex*cl_age # Equation 1 in the paper\\n    vc_wt <- (WT/100)^e_wt_vc # Not in the paper, based on Equation 2 in the paper\\n    vc  <- exp(lvc + etalvc)*vc_wt # Equation 5 in the paper\\n    \\n    Cc <- linCmt()\\n    Cc ~ prop(propSd)\\n  })\\n} #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Kovalenko_2020_dupilumab <- function() {\\n  description <- \"Dupilumab PK model (Kovalenko 2020)\"\\n  reference <- \"Kovalenko P, Davis JD, Li M, et al. Base and Covariate Population Pharmacokinetic Analyses of Dupilumab Using Phase 3 Data. Clinical Pharmacology in Drug Development. 2020;9(6):756-767. doi:10.1002/cpdd.780\"\\n  # Model 1 from table 1 and supplementary Table 2 in the publication and its\\n  # supplement.\\n  covariateData <-\\n    list(\\n      WT = \"Body weight in kg\"\\n    )\\n  ini({\\n    lvc <- log(2.48); label(\"central volume (L)\")\\n    lke <- log(0.0534); label(\"elimination rate (1/d)\")\\n    lkcp <- log(0.213); label(\"central-to-peripheral rate (1/d)\")\\n    Mpc <- 0.686; label(\"ratio of kcp and kpc (kpc is peripheral to central rate with units of 1/d)\")\\n    lka <- log(0.256); label(\"absorption rate (1/d)\")\\n    lMTT <- log(0.105); label(\"mean transit time (d)\")\\n    lVm <- log(1.07); label(\"maximum target-mediated rate of elimination (mg/L/d)\")\\n    Km <- fixed(0.01); label(\"Michaelis-Menten constant (mg/L)\")\\n    lfdepot <- log(0.643); label(\"Bioavailability (fraction)\")\\n    e_wt_vc <- 0.711; label(\"Exponent of weight on central volume (unitless)\")\\n\\n    etalvc ~ 0.192\\n    etalke ~ 0.285\\n    etalka ~ 0.474\\n    etalvm ~ 0.236\\n    etamtt ~ 0.525 # etamtt is assumed to be on log-scale MTT to prevent negative values; this is a difference relative to Supplementary Table 2\\n\\n    cppropSd <- 0.15; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- fixed(0.03); label(\"Additive residual error (mg/L)\")\\n  })\\n  model({\\n    # Weight normalization to 75 kg is assumed based on prior publication.  It\\n    # is not specified in the current publication:\\n    # Kovalenko P, DiCioccio AT, Davis JD, et al. Exploratory Population PK\\n    # Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against\\n    # IL-4Ralpha, in Atopic Dermatitis Patients and Normal Volunteers. CPT\\n    # Pharmacometrics Syst Pharmacol. 2016;5(11):617-624. doi:10.1002/psp4.12136\\n    vc <- exp(lvc + etalvc)*(WT/75)^e_wt_vc\\n    ke <- exp(lke + etalke)\\n    kcp <- exp(lkcp)\\n    ka <- exp(lka + etalka)\\n    MTT <- exp(lMTT + etamtt)\\n    Vm <- exp(lVm + etalvm)\\n\\n    # Derived parameters\\n    kpc <- kcp/Mpc\\n    ktr <- (3 + 1)/MTT\\n\\n    d/dt(depot) <- -ktr*depot\\n    d/dt(transit1) <- ktr*(depot - transit1)\\n    d/dt(transit2) <- ktr*(transit1 - transit2)\\n    d/dt(transit3) <- ktr*transit2 - ka*transit3\\n    # Linear and Michaelis-Menten clearance\\n    d/dt(central) <-                 ka*transit3 - ke*central - kcp*central + kpc*periph - central*(Vm/(Km + central/vc))\\n    d/dt(periph) <-                                             kcp*central - kpc*periph\\n\\n    f(depot) <- exp(lfdepot)\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to mg/L (measurement unit)\\n    Cc <- central/vc\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Soehoel_2022_tralokinumab <- function() {\\n  description <- \"Tralokinumab PK model (Soehoel 2022)\"\\n  reference <- \"Soehoel A, Larsen MS, Timmermann S. Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis. Clinical Pharmacology in Drug Development. 2022;11(8):910-921. doi:10.1002/cpdd.1113\"\\n  # From Table 2 footnotes\\n  covariateData <-\\n    list(\\n      nonECZTRA = \"1 = any study other than ECZTRA; 0 = ECZTRA study\",\\n      WT = \"Body weight in kg\",\\n      dilution = \"Was the drug diluted as it was in study D2213C00001? 1 = yes, 0 = no (0 is typical)\"\\n    )\\n  ini({\\n    lka <- log(0.184); label(\"Absorption rate (1/day)\")\\n    lvc <- log(2.71); label(\"Central volume of distribution (L)\")\\n    lcl <- log(0.149); label(\"Clearance (L/day)\")\\n    lvp <- log(1.44); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.159); label(\"Intercompartmental clearance (L/day)\")\\n    lfdepot <- log(0.761); label(\"Subcutaneous bioavailability (fraction)\")\\n    cpaddSd <- 0.238; label(\"Additive residual error (ug/mL)\")\\n    cppropSd <- 0.216; label(\"Proportional residual error (fraction)\")\\n\\n    e_wt_vcvp <- 0.793; label(\"Effect of body weight on central and peripheral volumes (unitless)\")\\n    e_wt_clq <- 0.873; label(\"Effect of body weight on clearance and intercompartmental clearance (unitless)\")\\n    e_nonECZTRA_cl <- 0.344; label(\"Effect of non-ECZTRA trials on clearance (unitless)\")\\n    e_nonECZTRA_vc <- 0.258; label(\"Effect of non-ECZTRA trials on central volume (unitless)\")\\n    e_f_dilution <- 0.354; label(\"Effect of dilution on bioavailability (unitless)\")\\n    e_ka_dilution <- -0.519; label(\"Effect of dilution trials on absorption rate (unitless)\")\\n\\n    etavc + etacl ~ c(0.386148, 0.2683494, 0.3057157)\\n  })\\n  model({\\n    fdepot <- exp(lfdepot)*(1 + e_f_dilution*dilution)\\n    ka <- exp(lka)*(1 + e_ka_dilution*dilution)\\n    cl <- exp(lcl + etacl)*(WT/75)^e_wt_clq * (1 + e_nonECZTRA_cl*nonECZTRA)\\n    vc <- exp(lvc + etavc)*(WT/75)^e_wt_vcvp * (1 + e_nonECZTRA_vc*nonECZTRA)\\n    q <- exp(lq)*(WT/75)^e_wt_clq\\n    vp <- exp(lvp)*(WT/75)^e_wt_vcvp\\n\\n    # No unit conversion is required to change mg/L (dosing amount/central\\n    # volume unit) to ug/mL (measurement unit)\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n}\\n\\n# etavc, etacl, and the covariance were calculated from the Table 2 footnotes\\n# as:\\n# etavc: sqrt(log(0.313^2 + 1)) = 0.386148\\n# etacl: sqrt(log(0.401^2 + 1)) = 0.3057157\\n# cov(etavc, etacl): sqrt(0.61*0.386148*0.3057157) #> 14 Zhu_2017_lebrikizumab <- function() {\\n  description <- \"Lebrikizumab PK model (Zhu 2017)\"\\n  reference <- \"Zhu R, Zheng Y, Dirks NL, et al. Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma. Pulmonary Pharmacology & Therapeutics. 2017;46:88-98. doi:10.1016/j.pupt.2017.08.010\"\\n  covariateData <-\\n    list(\\n      WT = \"Baseline body weight in kg\",\\n      AGE = \"Age in years\",\\n      SEXF = \"1 for female, 0 for male\",\\n      FORM_NS0 = \"Is the formulation NS0? 1 for yes, 0 for no (typically no)\",\\n      FORM_CHO_PHASE2 = \"Is the formulation CHO from Phase 2? 1 for yes, 0 for no (typically no)\",\\n      ADA = \"Is the subject ADA positive ever? 1 for yes, 0 for no\",\\n      RACE_BLACK = \"Is the race of the subject black or African American? 1 for yes, 0 for no\",\\n      RACE_ASIAN = \"Is the race of the subject Asian? 1 for yes, 0 for no\",\\n      RACE_OTHER = \"Is the race of the subject 'other'? 1 for yes, 0 for no\"\\n    )\\n  ini({\\n    lcl <- log(0.156); label(\"Clearance (L/day)\")\\n    lvc <- log(4.10); label(\"Central volume of distribution (L)\")\\n    lvp <- log(1.45); label(\"Peripheral volume of distribution (L)\")\\n    lq <- log(0.284); label(\"Intercompartmental clearance (L/day)\")\\n    lka <- log(0.239); label(\"Absorption rate (1/day)\")\\n    lfdepot <- log(0.856); label(\"Subcutaneous bioavailability (fraction)\")\\n\\n    e_cl_wt <- 1.00; label(\"Effect of body weight on clearance (unitless)\")\\n    e_vc_wt <- 0.814; label(\"Effect of body weight on central volume (unitless)\")\\n    e_vp_wt <- 0.692; label(\"Effect of body weight on peripheral volume (unitless)\")\\n    e_q_wt <- 0.479; label(\"Effect of body weight on intercompartmentl clearance (unitless)\")\\n    e_cl_age <- 0.0241; label(\"Effect of age on clearance (unitless)\")\\n    e_cl_sexf <- 1.06; label(\"Effect of sex on clearance (unitless)\")\\n    e_cl_race_black <- 1.07; label(\"Effect of race (black or African American) on clearance (unitless)\")\\n    e_cl_race_asian <- 1.09; label(\"Effect of race (Asian) on clearance (unitless)\")\\n    e_cl_race_other <- 1.11; label(\"Effect of race (other) on clearance (unitless)\")\\n    e_ka_form_nso <- 0.981; label(\"Effect of NSO formulation on absorption rate (unitless)\")\\n    e_ka_form_cho_phase2 <- 0.989; label(\"Effect of CHO formulation used during Phase 2 on absorption rate (unitless)\")\\n    e_f_form_nso <- 1.00; label(\"Effect of NSO formulation on bioavailability (unitless)\")\\n    e_f_form_cho_phase2 <- 0.973; label(\"Effect of CHO formulation used during Phase 2 on bioavailability (unitless)\")\\n    e_cl_ada_positive<- 1.04; label(\"Effect of anti-drug antibody (ADA) positivity on clearance (unitless)\")\\n\\n    # converted from covariance matrix reported in Table 3\\n    etacl + etavc + etaka ~\\n      c(\\n        0.32403703,\\n        0.28844410, 0.35213634,\\n        0.04505552, 0.06625708, 0.39242834\\n      )\\n\\n    cppropSd <- 0.0490; label(\"Proportional residual error (fraction)\")\\n    cpaddSd <- 0.00154; label(\"Additive residual error (ug/mL)\")\\n  })\\n  model({\\n    # Normalized continuous covariate values based on footnote to Table 3\\n    WTNORM <- WT/70\\n    AGENORM <- AGE/40\\n\\n    cl <-\\n      exp(lcl + etacl) *\\n      WTNORM^e_cl_wt * AGENORM^e_cl_age * e_cl_sexf^SEXF *\\n      e_cl_race_black^RACE_BLACK * e_cl_race_asian^RACE_ASIAN * e_cl_race_other^RACE_OTHER *\\n      e_cl_ada_positive^ADA\\n    vc <- exp(lvc + etavc) * WTNORM^e_vc_wt\\n    vp <- exp(lvp) * WTNORM^e_vp_wt\\n    q <- exp(lq) * WTNORM^e_q_wt\\n    ka <- exp(lka + etaka) * e_ka_form_nso^FORM_NSO * e_ka_form_cho_phase2^FORM_CHO_PHASE2\\n    fdepot <- exp(lfdepot) * e_f_form_nso^FORM_NSO * e_f_form_cho_phase2^FORM_CHO_PHASE2\\n    Cc <- linCmt()\\n    f(depot) <- fdepot\\n    Cc ~ add(cpaddSd) + prop(cppropSd)\\n  })\\n} #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     oncology_sdm_lobo_2002 <- function() {\\n  description <- \"Signal transduction model for delayed concentration effects on cancer cell growth\"\\n  reference <- \"Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: Application of a transit compartment model to characterize methotrexate effects in vitro. AAPS J. 2002;4(4):212-222. doi:10.1208/ps040442\"\\n  # Values for lkng, ltau, lec50, and kmax are for methotrexate from Lobo 2002,\\n  # Table 2.  propErr and addErr are added as reasonable values though not from\\n  # Lobo 2002 where no value is apparent in the paper.\\n  ini({\\n    lkng <- log(0.02) ; label(\"Cell net growth rate (growth minus death) (1/hr)\")\\n    ltau <- log(34.1) ; label(\"Mean transit time of each transit compartment (hr)\")\\n    lec50 <- log(0.1) ; label(\"Drug concentration reducing the cell growth by 50% (ug/mL)\")\\n    kmax <- 0.29 ; label(\"Maximum drug-related reduction in cell growth (1/hr)\")\\n\\n    tumorVolpropSd <- c(0, 0.3) ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- c(0, 50, 1000) ; label(\"Additive residual error (tumor volume units)\")\\n  })\\n  model({\\n    # cp is the drug concentration\\n    kng <- exp(lkng)\\n    tau <- exp(ltau)\\n    ec50 <- exp(lec50)\\n\\n    drugEffectTumorVol <- kmax*cp/(ec50 + cp)\\n\\n    tumorVol(0) <- tumorVol0\\n    d/dt(tumorVol) <- kng*tumorVol - transit4*tumorVol\\n    d/dt(transit1) <- (drugEffectTumorVol - transit1)/tau\\n    d/dt(transit2) <- (transit1 - transit2)/tau\\n    d/dt(transit3) <- (transit2 - transit3)/tau\\n    d/dt(transit4) <- (transit3 - transit4)/tau\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n} #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          oncology_xenograft_simeoni_2004 <- function() {\\n  description <- \"Oncology tumor growth model in xenograft models\"\\n  reference <- \"Monica Simeoni, Paolo Magni, Cristiano Cammia, Giuseppe De Nicolao, Valter Croci, Enrico Pesenti, Massimiliano Germani, Italo Poggesi, Maurizio Rocchetti; Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents. Cancer Res 1 February 2004; 64 (3): 1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524\"\\n  # Values for damageTransit (k1), drugSlope (k2), tumorExpGrowth (lambda0),\\n  # tumorLinGrowth (lambda1) are from paclitaxel experiment 1 reported in Table\\n  # 2 from the reference (limits are not from the reference). The values from\\n  # Table 2 will be estimated on the log scale to ensure positive values.\\n  # Residual errors are not in the original reference.\\n  ini({\\n    ldamageTransit <- log(c(0.1, 0.968, 10)) ; label(\"Transit rate through damage (1/day)\")\\n    ldrugSlope <- log(c(0.00001, 0.000629, 0.1)) ; label(\"Linear drug effect on cycling cells (1/(day*ng/mL))\")\\n    ltumorExpGrowth <- log(c(0.001, 0.273, 2)) ; label(\"Tumor exponential growth rate (1/day)\")\\n    ltumorLinGrowth <- log(c(0.01, 0.814, 5)) ; label(\"Tumor linear growth rate (tumor volume/day)\")\\n    tumorVolpropSd <- 0.2 ; label(\"Proportional residual error (fraction)\")\\n    tumorVoladdSd <- 30 ; label(\"Additive residual error (tumor volume)\")\\n  })\\n  model({\\n    damageTransit <- exp(ldamageTransit)\\n    drugSlope <- exp(ldrugSlope)\\n    tumorExpGrowth <- exp(ltumorExpGrowth)\\n    tumorLinGrowth <- exp(ltumorLinGrowth)\\n\\n    # tumorVol0 is provided in the data as the initial volume of the tumor.  It\\n    # can also be estimated.\\n    cyclingCells(0) <- tumorVol0\\n    psi <- 20 # psi is defined in the paper to cause a rapid switch between exponential and linear regimes\\n    tumorVol <- cyclingCells + damagedCells1 + damagedCells2 + damagedCells3\\n    # cp is provided in the data (or in an appended model) as the drug\\n    # concentration.  Units for cp will be apply to k2.\\n    drugEffectCyclingCells <- drugSlope*cp\\n    d/dt(cyclingCells) <- tumorExpGrowth*cyclingCells/(1 + (tumorExpGrowth/tumorLinGrowth * tumorVol)^psi)^(1/psi) - drugEffectCyclingCells*cyclingCells\\n    d/dt(damagedCells1) <- drugEffectCyclingCells*cyclingCells - damageTransit*damagedCells1\\n    d/dt(damagedCells2) <- damageTransit*(damagedCells1 - damagedCells2)\\n    d/dt(damagedCells3) <- damageTransit*(damagedCells2 - damagedCells3)\\n    tumorVol ~ prop(tumorVolpropSd) + add(tumorVoladdSd)\\n  })\\n}\\n\\nattr(oncology_xenograft_simeoni_2004, \"message\") <- \"You can modify the number of damaged cell compartments in the model using the function updateOncologyXenograftSimeoni2004(model, ncmt)\"\\noncology_xenograft_simeoni_2004 #>                                                                                                                 Description #> 1                                                                            One compartment PK model with linear clearance #> 2                                               One compartment PK model with linear clearance using differential equations #> 3                                                                            Two compartment PK model with linear clearance #> 4                                               Two compartment PK model with linear clearance using differential equations #> 5                                               Two compartment PK model with linear clearance using differential equations #> 6  Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model) #> 7                                                                          Three compartment PK model with linear clearance #> 8                                             Three compartment PK model with linear clearance using differential equations #> 9                                     Two compartment PK model with Michealis-Menten clearance using differential equations #> 10                            Two compartment PK model with linear clearance for average monoclonal antibodies (Davda 2014) #> 11                                                                Liraglutide PK model in adolescents (Carlsson Petri 2021) #> 12                                                                                      Dupilumab PK model (Kovalenko 2020) #> 13                                                                                     Tralokinumab PK model (Soehoel 2022) #> 14                                                                                         Lebrikizumab PK model (Zhu 2017) #> 15                                        Signal transduction model for delayed concentration effects on cancer cell growth #> 16                                                                          Oncology tumor growth model in xenograft models  # This will test the models in the catalog, set as_cran # to FALSE to test all the models. mtres = MB_test_catalog(state, as_cran=TRUE) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.R not found (skipping) #> ! MB: User-defined model: /Users/jmh/My Drive/projects/ruminate/github/ruminate/docs/reference/user_model.ctl not found (skipping) #> Warning: incomplete final line found on '/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/nlmixr2lib/modeldb/indirect_0cpt_transitEx.R' #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ✔ MB: PK_1cmt mtres$isgood #> [1] TRUE   # Creates a new empty element state = MB_new_element(state)  # Delete the current element state = MB_del_current_element(state)  # Fetch a list of the current element element = MB_fetch_current_element(state)  # This will attach a model to it: # Pulling the first model from the catalog fcn_def  = models[[\"summary\"]][1, ][[\"Model\"]] fcn_obj  = models[[\"summary\"]][1, ][[\"Object\"]] mdl_type = models[[\"summary\"]][1, ][[\"Type\"]] fcn_desc = models[[\"summary\"]][1, ][[\"Description\"]]  # This will build the rxode2 object from the model mk_rx_res = mk_rx_obj(   type  = mdl_type,   model = list(fcn_def = fcn_def,                fcn_obj = fcn_obj)) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # This will attach the model to the current element element = MB_update_model(   state       = state,   session     = session,   current_ele = element,   rx_obj      = mk_rx_res[[\"capture\"]][[\"rx_obj\"]],   note        = fcn_desc,   reset       = TRUE)  # You can now place element back in the state state = MB_set_current_element(state, element) #> → MB: model checksum updated: 712c5ad5044dd7388ebda219880957b0 #> → MB: module checksum updated:895717e3ba4dd19b9eff5100cd1627dc  # This will fetch the current component component = MB_fetch_component(state, element)  # You can use the component to build the code to generate the model: gen_code =   MB_build_code(state        = state, session = session,               fcn_def      = component[[\"fcn_def\"]],               fcn_obj_name = \"my_fcn_obj\",               rx_obj_name  = \"my_obj_name\")  # Model code to be included in a larger script message(paste0(gen_code$model_code, collapse=\"\\n\")) #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # Stand-alone model code message(paste0(gen_code$model_code_sa, collapse=\"\\n\")) #> library(\"nlmixr2lib\") #> library(\"nonmem2rx\") #> library(\"rxode2\") #>  #> my_fcn_obj = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> my_obj_name =  rxode2::rxode2(my_fcn_obj)  # This will fetch the code to regenerate all of the components of this module message(MB_fetch_code(state)) #> # Base model: manual #> MB_obj_1_fcn = function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         Cc <- linCmt() #>         Cc ~ prop(propSd) #>     }) #> } #> MB_obj_1_rx =  rxode2::rxode2(MB_obj_1_fcn) #>  #>  #> # Base model: user #> MB_obj_2_fcn = function ()  #> { #>     ini({ #>         TV_F1 <- fix(2.22044604925031e-16, 0.744, Inf) #>         TV_ka <- c(2.22044604925031e-16, 0.282, Inf) #>         TV_CL <- c(-36.0436533891172, -1.6094379124341, 709.782712893384) #>         TV_Vc <- c(-36.0436533891172, 1.28370777234479, 709.782712893384) #>         TV_Vp <- c(-36.0436533891172, 1.01160091167848, 709.782712893384) #>         TV_Q <- c(-36.0436533891172, -0.29169009384932, 709.782712893384) #>         TV_MW <- fix(2.22044604925031e-16, 140, Inf) #>         TV_BM_IC <- c(2.22044604925031e-16, 1000, Inf) #>         TV_kdeg_BM <- fix(2.22044604925031e-16, 0.1, Inf) #>         TV_Emax <- c(2.22044604925031e-16, 100, Inf) #>         TV_EC50 <- fix(2.22044604925031e-16, 300, Inf) #>         prop_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_PK <- c(2.22044604925031e-16, 0.1, Inf) #>         add_err_BM <- c(2.22044604925031e-16, 0.1, Inf) #>         ETAka ~ 0.416 #>         ETACL + ETAVc + ETAVp ~ c(0.09875, 0.0786, 0.116, 0.0619,  #>             0.0377, 0.0789) #>         ETAQ ~ 0.699 #>         ETABM_IC ~ 0.05 #>         ETAEmax ~ 0.1 #>     }) #>     model({ #>         F1 = TV_F1 #>         ka = TV_ka * exp(ETAka) #>         CL = exp(TV_CL) * exp(ETACL) #>         Vc = exp(TV_Vc) * exp(ETAVc) #>         Cc = Ac/(Vc) #>         Vp = exp(TV_Vp) * exp(ETAVp) #>         Q = exp(TV_Q) * exp(ETAQ) #>         MW = TV_MW #>         BM_IC = TV_BM_IC * exp(ETABM_IC) #>         kdeg_BM = TV_kdeg_BM #>         Emax = TV_Emax * exp(ETAEmax) #>         EC50 = TV_EC50 #>         Dinf = 0 #>         WTTV = 70 #>         CL_IND = CL * (1 + SEX_ID * 0.1) * (1 + SUBTYPE_ID *  #>             0.1) #>         kel = CL_IND/Vc * ((WT/WTTV))^(-0.35) #>         kcp = Q/Vc * ((WT/WTTV))^(-0.35) #>         kpc = Q/Vp * ((WT/WTTV))^(-0.35) #>         ksyn_BM = kdeg_BM * BM_IC #>         STIM = 1 + Emax * Cc/(EC50 + Cc) #>         BM(0) = BM_IC #>         d/dt(At) = (-ka * At) #>         d/dt(Ac) = ((ka * At * F1/Vc - kel * Cc - kcp * Cc +  #>             kpc * Cp * Vp/Vc + Dinf/Vc)) * Vc #>         d/dt(Cp) = (kcp * Cc * Vc/Vp - kpc * Cp) #>         d/dt(BM) = ksyn_BM * STIM - (kdeg_BM * BM) #>         C_ng_ml = Cc * MW #>         C_ng_ml ~ add(add_err_PK) + prop(prop_err_PK) #>         BM_obs = BM #>         BM_obs ~ add(add_err_BM) #>     }) #> } #> MB_obj_2_rx =  rxode2::rxode2(MB_obj_2_fcn) #>"},{"path":"/reference/NCA_Server.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Non-Compartmental Analysis State — NCA_Server","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"Merges default app options changes made UI","code":""},{"path":"/reference/NCA_Server.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"","code":"NCA_Server(   id,   FM_yaml_file = system.file(package = \"formods\", \"templates\", \"formods.yaml\"),   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\"),   id_ASM = \"ASM\",   id_UD = \"UD\",   id_DW = \"DW\",   deployed = FALSE,   react_state = NULL )"},{"path":"/reference/NCA_Server.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"id ID string corresponds ID used call modules UI elements FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id_ASM ID string app state management module used save load app states id_UD ID string upload data module used save load app states id_DW ID string data wrangling module used save load app states deployed Boolean variable indicating whether app deployed . react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/NCA_Server.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. NCA: isgood: Boolean object indicating file successfully loaded. checksum: MD5 sum contents element can used detect changes state. MOD_TYPE: Character data containing type module \"NCA\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/NCA_Server.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Non-Compartmental Analysis State — NCA_Server","text":"","code":"if(interactive()){ # original file: inst/templates/ruminate.R library(formods) library(ruminate)  # These are suggested packages library(shinydashboard) #library(ggpubr) #library(plotly) #library(shinybusy) library(prompter) #library(utils)  tags$style(\"@import url(https://use.fontawesome.com/releases/v6.4.0/css/all.css);\")  # You can copy these locally and customize them for your own needs. Simply # change the assignment to the local copy you've modified. formods.yaml  = system.file(package=\"formods\",  \"templates\",  \"formods.yaml\") ASM.yaml      = system.file(package=\"formods\",  \"templates\",  \"ASM.yaml\") UD.yaml       = system.file(package=\"formods\",  \"templates\",  \"UD.yaml\") DW.yaml       = system.file(package=\"formods\",  \"templates\",  \"DW.yaml\") FG.yaml       = system.file(package=\"formods\",  \"templates\",  \"FG.yaml\") NCA.yaml      = system.file(package=\"ruminate\", \"templates\",  \"NCA.yaml\")   # Name of  file to indicate we need to load testing data ftmptest = file.path(tempdir(), \"ruminate.test\")   # Making sure that the deployed object is created if(!exists(\"deployed\")){   deployed = FALSE }  # Making sure that the run_dev object is created if(file.exists(file.path(tempdir(), \"RUMINTE_DEVELOPMENT\"))){   run_dev  = TRUE }else{   run_dev  = FALSE }  # If the SETUP.R file exists we source it  if(file.exists(\"SETUP.R\")){   source(\"SETUP.R\") }   # If the DEPLOYED file marker existrs we set deployed to TRUE if(file.exists(\"DEPLOYED\")){   deployed = TRUE }  CSS <- \" .wrapfig {   float: right;   shape-margin: 20px;   margin-right: 20px;   margin-bottom: 20px; } \"  #https://fontawesome.com/icons?from=io logo_url =   \"https://raw.githubusercontent.com/john-harrold/ruminate/main/man/figures/logo.png\" data_url =   \"https://github.com/john-harrold/formods/raw/master/inst/test_data/TEST_DATA.xlsx\" run_url  =   \"https://runruminate.ubiquity.tools/\" use_url  =   \"https://useruminate.ubiquity.tools/\" main_url =   \"https://ruminate.ubiquity.tools/\" issue_url =   \"https://github.com/john-harrold/ruminate/issues\"  intro_text = tags$p( \"Ruminate is a shiny module for pharmacometric data processing, visualization, and analysis. It consists of separate shiny modules that provide interfaces into common R packages and provides the underlying code. This is done to facilitate usage of those packages and to provide reproducible analyses.\" , tags$li( \"To find out more visit  \",         tags$a(\"ruminate.ubiquity.tools\", href=main_url),\"\"), tags$li( \"To give it a try you can download a test dataset \",         tags$a(\"here\", href=data_url),\"\"), tags$li( \"Go to  \",         tags$a(\"useruminate.ubiquity.tools\", href=use_url),\" for a video         demonstrating how to use ruminate\"), tags$li( \"If you run into any problems, have questions, or want a feature please         visit the \",         tags$a(\"issues\", href=issue_url),\" page\") )   ui <- shinydashboard::dashboardPage(   skin=\"black\",   shinydashboard::dashboardHeader(title=\"ruminate\"),   shinydashboard::dashboardSidebar(      shinydashboard::sidebarMenu(        shinydashboard::menuItem(\"Load/Save\",                                 tabName=\"loadsave\",                                 icon=icon(\"arrow-down-up-across-line\")) ,        shinydashboard::menuItem(\"Transform Data\", tabName=\"wrangle\", icon=icon(\"shuffle\")),        shinydashboard::menuItem(\"Visualize\",      tabName=\"plot\",    icon=icon(\"chart-line\")),        shinydashboard::menuItem(\"NCA\",            tabName=\"nca\",     icon=icon(\"chart-area\")),        shinydashboard::menuItem(\"App Info\",       tabName=\"sysinfo\", icon=icon(\"book-medical\"))      )   ),   shinydashboard::dashboardBody(   tags$head(     tags$style(HTML(CSS))   ),     shinydashboard::tabItems(        shinydashboard::tabItem(tabName=\"nca\",                shinydashboard::box(title=\"Run Non-Compartmental Analysis\", width=12,                fluidRow( prompter::use_prompt(),                column(width=12,                htmlOutput(NS(\"NCA\",  \"NCA_ui_compact\")))))                ),        shinydashboard::tabItem(tabName=\"loadsave\",          #     shinydashboard::box(title=NULL, width=12,                shinydashboard::tabBox(                  width = 12,                  title = NULL,                  shiny::tabPanel(id=\"load_data\",                           title=tagList(shiny::icon(\"file-arrow-up\"),                                         \"Load Data\"),                    fluidRow(                      column(width=6,                        div(style=\"display:inline-block;width:100%\",                        htmlOutput(NS(\"UD\", \"ui_ud_load_data\"))),                        htmlOutput(NS(\"UD\", \"ui_ud_clean\")),                        htmlOutput(NS(\"UD\", \"ui_ud_select_sheets\")),                        htmlOutput(NS(\"UD\", \"ui_ud_text_load_result\"))),                      column(width=6,                          tags$p(                              tags$img(                              class = \"wrapfig\",                              src   = logo_url,                              width = 150,                              alt = \"formods logo\" ),                          intro_text                          ))                    ),                  fluidRow(                    column(width=12,                           div(style=\"display:inline-block;vertical-align:top\",                                     htmlOutput(NS(\"UD\", \"ui_ud_data_preview\")))                           ))                  ),                  shiny::tabPanel(id=\"save_state\",                           title=tagList(shiny::icon(\"arrow-down-up-across-line\"),                                         \"Save or Load Analysis\"),                  fluidRow(                    column(width=5,                           div(style=\"display:inline-block;vertical-align:top\",                    htmlOutput(NS(\"ASM\", \"ui_asm_compact\"))                    ))                    )                  )                )          #     ),                ),        shinydashboard::tabItem(tabName=\"wrangle\",                shinydashboard::box(title=\"Transform and Create Views of Your Data\", width=12,                fluidRow(                column(width=12,                htmlOutput(NS(\"DW\",  \"DW_ui_compact\")))))                ),        shinydashboard::tabItem(tabName=\"plot\",                shinydashboard::box(title=\"Visualize Data\", width=12,                htmlOutput(NS(\"FG\",  \"FG_ui_compact\")))),        shinydashboard::tabItem(tabName=\"sysinfo\",          #     box(title=\"System Details\", width=12,                shinydashboard::tabBox(                  width = 12,                  title = NULL,                  shiny::tabPanel(id=\"sys_packages\",                           title=tagList(shiny::icon(\"box-open\"),                                         \"Installed Packages\"),                  htmlOutput(NS(\"ASM\", \"ui_asm_sys_packages\"))                  ),                  shiny::tabPanel(id=\"sys_modules\",                           title=tagList(shiny::icon(\"cubes\"),                                         \"Loaded Modules\"),                  htmlOutput(NS(\"ASM\", \"ui_asm_sys_modules\"))                  ),                  shiny::tabPanel(id=\"sys_log\",                           title=tagList(shiny::icon(\"clipboard-list\"),                                         \"Log\"),                  verbatimTextOutput(NS(\"ASM\", \"ui_asm_sys_log\"))                  ),                  shiny::tabPanel(id=\"sys_options\",                           title=tagList(shiny::icon(\"sliders\"),                                         \"R Options\"),                  htmlOutput(NS(\"ASM\", \"ui_asm_sys_options\"))                  )          #       )                ))       )     )   )  # Main app server server <- function(input, output, session) {    # Empty reactive object to track and react to   # changes in the module state outside of the module   react_FM = reactiveValues()    # Module IDs and the order they are needed for code generation   mod_ids = c(\"UD\", \"DW\", \"FG\", \"NCA\", \"MB\")    # If the ftmptest file is present we load test data   if(file.exists(ftmptest)){     NCA_test_mksession(       session,       id     = \"NCA\",       id_UD  = \"UD\",       id_DW  = \"DW\",       id_ASM = \"ASM\"     )   }    # Module servers   formods::ASM_Server( id=\"ASM\",                        deployed      = deployed,                        react_state   = react_FM,                        FM_yaml_file  = formods.yaml,                        MOD_yaml_file = ASM.yaml,                        mod_ids       = mod_ids)   formods::UD_Server(  id =\"UD\", id_ASM = \"ASM\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = UD.yaml,                        FM_yaml_file     = formods.yaml)   formods::DW_Server(  id=\"DW\",       id_ASM = \"ASM\",                        id_UD = \"UD\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = DW.yaml,                        FM_yaml_file     = formods.yaml)   formods::FG_Server(  id=\"FG\",     id_ASM = \"ASM\",                        id_UD = \"UD\", id_DW = \"DW\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = FG.yaml,                        FM_yaml_file     = formods.yaml)   ruminate::NCA_Server(id    =\"NCA\", id_ASM = \"ASM\",                        id_UD = \"UD\", id_DW  = \"DW\",                        deployed         = deployed,                        react_state      = react_FM,                        MOD_yaml_file    = NCA.yaml,                        FM_yaml_file     = formods.yaml)  }  shinyApp(ui, server) }"},{"path":"/reference/NCA_add_int.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds Analysis Interval to Current Analysis — NCA_add_int","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"Takes start time, stop time, NCA parameters adds intervals table","code":""},{"path":"/reference/NCA_add_int.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"","code":"NCA_add_int(state, interval_start, interval_stop, nca_parameters)"},{"path":"/reference/NCA_add_int.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"state NCA state NCA_fetch_state() interval_start Interval start time (numeric). interval_stop Interval stop time (numeric). nca_parameters list NCA parameters interval","code":""},{"path":"/reference/NCA_add_int.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds Analysis Interval to Current Analysis — NCA_add_int","text":"State interval added current analysis.","code":""},{"path":"/reference/NCA_append_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Report Elements — NCA_append_report","title":"Append Report Elements — NCA_append_report","text":"Takes NCA state object appends reportable elements specified report type. NCA analyses \"good\" state reported. good state ignored.","code":""},{"path":"/reference/NCA_append_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Report Elements — NCA_append_report","text":"","code":"NCA_append_report(state, rpt, rpttype, gen_code_only = FALSE)"},{"path":"/reference/NCA_append_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Report Elements — NCA_append_report","text":"state NCA state NCA_fetch_state() rpt Report current content report appended function. details structure see documentation FM_generate_report. rpttype Type report generate (supported \"xlsx\", \"pptx\", \"docx\"). gen_code_only Boolean value indicating code generated (FALSE).","code":""},{"path":"/reference/NCA_append_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Report Elements — NCA_append_report","text":"list containing following elements isgood:    Return status function. hasrptele: Boolean indicator module reportable elements. code:      Code create report elements. msgs:      Messages passed back user. rpt:       Report additions passed back user.","code":""},{"path":[]},{"path":"/reference/NCA_append_report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append Report Elements — NCA_append_report","text":"","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:f47a740b0a21e148117be9dbd00ce49c state = sess_res$state  # here we need an empty report object for tabular data rpt = list(summary = list(), sheets=list())  # Now we append the report indicating we want # Excel output: rpt_res = NCA_append_report(state,  rpt           = rpt,  rpttype       = \"xlsx\",  gen_code_only = TRUE)  # Shows if report elements are present rpt_res$hasrptele #> [1] TRUE  # Code chunk to generate report element cat(paste(rpt_res$code, collapse=\"\\n\")) #> rpt[[\"sheets\"]][[\"NCA_1_RES\"]]=NCA_1_table_ind_params[[\"one_table\"]] #> rpt[[\"summary\"]] = rbind(rpt[[\"summary\"]], #>   data.frame( #>     Sheet_Name=\"NCA_1_RES\", #>     Description=\"PK NCA Analysis Example (indiviudal results)\" #>   ) #> ) #> rpt[[\"sheets\"]][[\"NCA_1_RAW\"]]=as.data.frame(NCA_1_res) #> rpt[[\"summary\"]] = rbind(rpt[[\"summary\"]], #>   data.frame( #>     Sheet_Name=\"NCA_1_RAW\", #>     Description=\"PK NCA Analysis Example (raw NCA results)\" #>   ) #> )"},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"Compiles Metadata PKNCA","code":""},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"","code":"NCA_fetch_PKNCA_meta()"},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"Dataframe containing PKCNA metadata NCA parameters.","code":""},{"path":"/reference/NCA_fetch_PKNCA_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches PKNCA Metadata — NCA_fetch_PKNCA_meta","text":"","code":"PKNCA_meta = NCA_fetch_PKNCA_meta() utils::head(PKNCA_meta) #> $parameters #>                parameter       unit_type #> 1                  start            time #> 2                    end            time #> 3                auclast             auc #> 4                 aucall             auc #> 5               aumclast            aumc #> 6                aumcall            aumc #> 7            aucint.last             auc #> 8       aucint.last.dose             auc #> 9             aucint.all             auc #> 10       aucint.all.dose             auc #> 11                    c0            conc #> 12                  cmax            conc #> 13                  cmin            conc #> 14                  tmax            time #> 15                 tlast            time #> 16                tfirst            time #> 17             clast.obs            conc #> 18               cl.last       clearance #> 19                cl.all       clearance #> 20                     f        fraction #> 21              mrt.last            time #> 22           mrt.iv.last            time #> 23              vss.last          volume #> 24           vss.iv.last          volume #> 25                   cav            conc #> 26               ctrough            conc #> 27                cstart            conc #> 28                   ptr        fraction #> 29                  tlag            time #> 30              deg.fluc               % #> 31                 swing               % #> 32                  ceoi            conc #> 33  aucabove.predose.all             auc #> 34   aucabove.trough.all             auc #> 35                    ae          amount #> 36              clr.last renal_clearance #> 37               clr.obs renal_clearance #> 38              clr.pred renal_clearance #> 39                    fe        fraction #> 40        sparse_auclast             auc #> 41            time_above            time #> 42             aucivlast             auc #> 43              aucivall             auc #> 44         aucivint.last             auc #> 45          aucivint.all             auc #> 46        aucivpbextlast               % #> 47         aucivpbextall               % #> 48    aucivpbextint.last               % #> 49     aucivpbextint.all               % #> 50             half.life            time #> 51             r.squared        unitless #> 52         adj.r.squared        unitless #> 53              lambda.z    inverse_time #> 54   lambda.z.time.first            time #> 55     lambda.z.n.points           count #> 56            clast.pred            conc #> 57            span.ratio        fraction #> 58        thalf.eff.last            time #> 59     thalf.eff.iv.last            time #> 60              kel.last    inverse_time #> 61           kel.iv.last    inverse_time #> 62            aucinf.obs             auc #> 63           aucinf.pred             auc #> 64           aumcinf.obs            aumc #> 65          aumcinf.pred            aumc #> 66        aucint.inf.obs             auc #> 67   aucint.inf.obs.dose             auc #> 68       aucint.inf.pred             auc #> 69  aucint.inf.pred.dose             auc #> 70          aucivinf.obs             auc #> 71         aucivinf.pred             auc #> 72     aucivpbextinf.obs               % #> 73    aucivpbextinf.pred               % #> 74           aucpext.obs               % #> 75          aucpext.pred               % #> 76                cl.obs       clearance #> 77               cl.pred       clearance #> 78               mrt.obs            time #> 79              mrt.pred            time #> 80            mrt.iv.obs            time #> 81           mrt.iv.pred            time #> 82            mrt.md.obs            time #> 83           mrt.md.pred            time #> 84                vz.obs          volume #> 85               vz.pred          volume #> 86               vss.obs          volume #> 87              vss.pred          volume #> 88            vss.iv.obs          volume #> 89           vss.iv.pred          volume #> 90            vss.md.obs          volume #> 91           vss.md.pred          volume #> 92                vd.obs          volume #> 93               vd.pred          volume #> 94         thalf.eff.obs            time #> 95        thalf.eff.pred            time #> 96      thalf.eff.iv.obs            time #> 97     thalf.eff.iv.pred            time #> 98               kel.obs    inverse_time #> 99              kel.pred    inverse_time #> 100           kel.iv.obs    inverse_time #> 101          kel.iv.pred    inverse_time #> 102           auclast.dn    auc_dosenorm #> 103            aucall.dn    auc_dosenorm #> 104        aucinf.obs.dn    auc_dosenorm #> 105       aucinf.pred.dn    auc_dosenorm #> 106          aumclast.dn   aumc_dosenorm #> 107           aumcall.dn   aumc_dosenorm #> 108       aumcinf.obs.dn   aumc_dosenorm #> 109      aumcinf.pred.dn   aumc_dosenorm #> 110              cmax.dn   conc_dosenorm #> 111              cmin.dn   conc_dosenorm #> 112         clast.obs.dn   conc_dosenorm #> 113        clast.pred.dn   conc_dosenorm #> 114               cav.dn   conc_dosenorm #> 115           ctrough.dn   conc_dosenorm #>                                                 pretty_name data_type #> 1                                            Interval Start  interval #> 2                                              Interval End  interval #> 3                                                   AUClast  interval #> 4                                                    AUCall  interval #> 5                                                 AUMC,last  interval #> 6                                                  AUMC,all  interval #> 7                   AUCint (based on AUClast extrapolation)  interval #> 8       AUCint (based on AUClast extrapolation, dose-aware)  interval #> 9                    AUCint (based on AUCall extrapolation)  interval #> 10       AUCint (based on AUCall extrapolation, dose-aware)  interval #> 11                                                       C0  interval #> 12                                                     Cmax  interval #> 13                                                     Cmin  interval #> 14                                                     Tmax  interval #> 15                                                    Tlast  interval #> 16                                                   Tfirst  interval #> 17                                                    Clast  interval #> 18                                    CL (based on AUClast)  interval #> 19                                     CL (based on AUCall)  interval #> 20                                          Bioavailability  interval #> 21                                   MRT (based on AUClast)  interval #> 22                    MRT (for IV dosing, based on AUClast)  interval #> 23                                   Vss (based on AUClast)  interval #> 24                    Vss (for IV dosing, based on AUClast)  interval #> 25                                                      Cav  interval #> 26                                                  Ctrough  interval #> 27                                                   Cstart  interval #> 28                                     Peak-to-trough ratio  interval #> 29                                                     Tlag  interval #> 30                                    Degree of fluctuation  interval #> 31                                                    Swing  interval #> 32                                                     Ceoi  interval #> 33                                                AUC,above  interval #> 34                                                AUC,above  interval #> 35                                          Amount excreted  interval #> 36                           Renal clearance (from AUClast)  interval #> 37                        Renal clearance (from AUCinf,obs)  interval #> 38                       Renal clearance (from AUCinf,pred)  interval #> 39                                        Fraction excreted  interval #> 40                                           Sparse AUClast  interval #> 41                                 Time above Concentration  interval #> 42                                      AUClast (IV dosing)  interval #> 43                                       AUCall (IV dosing)  interval #> 44                                  AUCint,last (IV dosing)  interval #> 45                                   AUCint,all (IV dosing)  interval #> 46                               AUCbext (based on AUClast)  interval #> 47                                AUCbext (based on AUCall)  interval #> 48                           AUCbext (based on AUCint,last)  interval #> 49                            AUCbext (based on AUCint,all)  interval #> 50                                                Half-life  interval #> 51                                                    $r^2$  interval #> 52                                              $r^2_{adj}$  interval #> 53                                             $\\\\lambda_z$  interval #> 54                              First time for $\\\\lambda_z$  interval #> 55                       Number of points used for lambda_z  interval #> 56                                               Clast,pred  interval #> 57                                               Span ratio  interval #> 58                  Effective half-life (based on MRT,last)  interval #> 59    Effective half-life (for IV dosing, based on MRTlast)  interval #> 60                                   Kel (based on AUClast)  interval #> 61                    Kel (for IV dosing, based on AUClast)  interval #> 62                                               AUCinf,obs  interval #> 63                                              AUCinf,pred  interval #> 64                                             AUMC,inf,obs  interval #> 65                                            AUMC,inf,pred  interval #> 66               AUCint (based on AUCinf,obs extrapolation)  interval #> 67   AUCint (based on AUCinf,obs extrapolation, dose-aware)  interval #> 68              AUCint (based on AUCinf,pred extrapolation)  interval #> 69  AUCint (based on AUCinf,pred extrapolation, dose-aware)  interval #> 70                                   AUCinf,obs (IV dosing)  interval #> 71                                  AUCinf,pred (IV dosing)  interval #> 72                            AUCbext (based on AUCinf,obs)  interval #> 73                           AUCbext (based on AUCinf,pred)  interval #> 74                            AUCpext (based on AUCinf,obs)  interval #> 75                           AUCpext (based on AUCinf,pred)  interval #> 76                                 CL (based on AUCinf,obs)  interval #> 77                                CL (based on AUCinf,pred)  interval #> 78                                MRT (based on AUCinf,obs)  interval #> 79                               MRT (based on AUCinf,pred)  interval #> 80                 MRT (for IV dosing, based on AUCinf,obs)  interval #> 81                MRT (for IV dosing, based on AUCinf,pred)  interval #> 82           MRT (for multiple dosing, based on AUCinf,obs)  interval #> 83          MRT (for multiple dosing, based on AUCinf,pred)  interval #> 84                                 Vz (based on AUCinf,obs)  interval #> 85                                Vz (based on AUCinf,pred)  interval #> 86                                Vss (based on AUCinf,obs)  interval #> 87                               Vss (based on AUCinf,pred)  interval #> 88                 Vss (for IV dosing, based on AUCinf,obs)  interval #> 89                Vss (for IV dosing, based on AUCinf,pred)  interval #> 90              Vss (for multiple-dose, based on Clast,obs)  interval #> 91             Vss (for multiple-dose, based on Clast,pred)  interval #> 92                                  Vd (based on Clast,obs)  interval #> 93                                 Vd (based on Clast,pred)  interval #> 94                   Effective half-life (based on MRT,obs)  interval #> 95                  Effective half-life (based on MRT,pred)  interval #> 96    Effective half-life (for IV dosing, based on MRT,obs)  interval #> 97   Effective half-life (for IV dosing, based on MRT,pred)  interval #> 98                                Kel (based on AUCinf,obs)  interval #> 99                               Kel (based on AUCinf,pred)  interval #> 100                Kel (for IV dosing, based on AUCinf,obs)  interval #> 101               Kel (for IV dosing, based on AUCinf,pred)  interval #> 102                               AUClast (dose-normalized)  interval #> 103                                AUCall (dose-normalized)  interval #> 104                            AUCinf,obs (dose-normalized)  interval #> 105                           AUCinf,pred (dose-normalized)  interval #> 106                             AUMC,last (dose-normalized)  interval #> 107                              AUMC,all (dose-normalized)  interval #> 108                          AUMC,inf,obs (dose-normalized)  interval #> 109                         AUMC,inf,pred (dose-normalized)  interval #> 110                                  Cmax (dose-normalized)  interval #> 111                                  Cmin (dose-normalized)  interval #> 112                                 Clast (dose-normalized)  interval #> 113                            Clast,pred (dose-normalized)  interval #> 114                                   Cav (dose-normalized)  interval #> 115                               Ctrough (dose-normalized)  interval #>                                                                                                                                                                                                                                                                                                                                                              desc #> 1                                                                                                                                                                                                                                                                                                                                   Starting time of the interval #> 2                                                                                                                                                                                                                                                                                                              Ending time of the interval (potentially infinity) #> 3                                                                                                                                                                                                                      The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 4                                                                                                     The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 5                                                                                                                                                                                                               The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification #> 6                                                                                The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification plus the moment of the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 7                                                                                                                                                                                                                                  The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 8                                                                                                                                                                    The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 9                                                                                                                                                                           The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 10                                                                                                            The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 11                                                                                                                                                                                                                                                                                                                        Initial concentration after an IV bolus #> 12                                                                                                                                                                                                                                                                                                                                 Maximum observed concentration #> 13                                                                                                                                                                                                                                                                                                                                 Minimum observed concentration #> 14                                                                                                                                                                                                                                                                                                                     Time of the maximum observed concentration #> 15                                                                                                                                                                                                                                                                                      Time of the last concentration observed above the limit of quantification #> 16                                                                                                                                                                                                                                                                                              Time of the first concentration above the limit of quantification #> 17                                                                                                                                                                                                                                                                                              The last concentration observed above the limit of quantification #> 18                                                                                                                                                                                                                                                                                                       Clearance or observed oral clearance calculated to Clast #> 19                                                                                                                                                                                                                                                                                                    Clearance or observed oral clearance calculated with AUCall #> 20                                                                                                                                                                                                                                                                                                                    Bioavailability or relative bioavailability #> 21                                                                                                                                                                                                                                                                                       The mean residence time to the last observed concentration above the LOQ #> 22                                                                                                                                                                                                                                                        The mean residence time to the last observed concentration above the LOQ correcting for dosing duration #> 23                                                                                                                                                                                                                                                                                              The steady-state volume of distribution calculating through Tlast #> 24                                                                                                                                                                                                                                                                    The steady-state volume of distribution with intravenous infusion calculating through Tlast #> 25                                                                                                                                                                                                                                                                                                                   The average concentration during an interval #> 26                                                                                                                                                                                                                                                                                                                     The trough (end of interval) concentration #> 27                                                                                                                                                                                                                                                                                                                                      The predose concentration #> 28                                                                                                                                                                                                                                                                                                                                Peak-to-Trough ratio (fraction) #> 29                                                                                                                                                                                                                                                                                                                                                       Lag time #> 30                                                                                                                                                                                                                                                                                                                                          Degree of fluctuation #> 31                                                                                                                                                                                                                                                                                                                                         Swing relative to Cmin #> 32                                                                                                                                                                                                                                                                                                                           Concentration at the end of infusion #> 33  The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero #> 34  The area under the concentration time the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification, with a concentration subtracted from all concentrations and values below zero after subtraction set to zero #> 35                                                                                                                                                                                                                                                                                                            The amount excreted (typically into urine or feces) #> 36                                                                                                                                                                                                                                                                                                                   The renal clearance calculated using AUClast #> 37                                                                                                                                                                                                                                                                                                                The renal clearance calculated using AUCinf,obs #> 38                                                                                                                                                                                                                                                                                                               The renal clearance calculated using AUCinf,pred #> 39                                                                                                                                                                                                                                                                                                                              The fraction of the dose excreted #> 40                                                                                                                                                                                             For sparse PK sampling, the area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 41                                                                                                                                                                                                                                                                                                                               Time above a given concentration #> 42                                                                                                                                                                                                                                                                    The AUClast calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 43                                                                                                                                                                                                                                                                     The AUCall calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 44                                                                                                                                                                                                                                                                The AUCint,last calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 45                                                                                                                                                                                                                                                                 The AUCint,all calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 46                                                                                                                                                                                                                                                                                         The back-extrapolation percent for intravenous dosing based on AUClast #> 47                                                                                                                                                                                                                                                                                          The back-extrapolation percent for intravenous dosing based on AUCall #> 48                                                                                                                                                                                                                                                                                     The back-extrapolation percent for intravenous dosing based on AUCint,last #> 49                                                                                                                                                                                                                                                                                      The back-extrapolation percent for intravenous dosing based on AUCint,all #> 50                                                                                                                                                                                                                                                                                                                                       The (terminal) half-life #> 51                                                                                                                                                                                                                                                                                                                     The r^2 value of the half-life calculation #> 52                                                                                                                                                                                                                                                                                                            The adjusted r^2 value of the half-life calculation #> 53                                                                                                                                                                                                                                                                                                                 The elimination rate of the terminal half-life #> 54                                                                                                                                                                                                                                                                                                     The first time point used for the calculation of half-life #> 55                                                                                                                                                                                                                                                                                                     The number of points used for the calculation of half-life #> 56                                                                                                                                                                                                                                                                                                       The concentration at Tlast as predicted by the half-life #> 57                                                                                                                                                                                                                                                                                      The ratio of the half-life to the duration used for half-life calculation #> 58                                                                                                                                                                                                                                                                                                       The effective half-life (as determined from the MRTlast) #> 59                                                                                                                                                                                                                                                                                           The effective half-life (as determined from the intravenous MRTlast) #> 60                                                                                                                                                                                                                                                                                                    Elimination rate (as calculated from the MRT using AUClast) #> 61                                                                                                                                                                                                                                                                                                  Elimination rate (as calculated from the intravenous MRTlast) #> 62                                                                                                                                                                                                              The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 63                                                                                                                                                                                                             The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast #> 64                                                                                                                                                                                                       The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 65                                                                                                                                                                                                      The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the predicted Clast #> 66                                                                                                                                                                                                                                 The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 67                                                                                                                                                                   The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 68                                                                                                                                                                          The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 69                                                                                                            The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 70                                                                                                                                                                                                                                                                 The AUCinf,obs calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 71                                                                                                                                                                                                                                                                           The  calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 72                                                                                                                                                                                                                                                                                      The back-extrapolation percent for intravenous dosing based on AUCinf,obs #> 73                                                                                                                                                                                                                                                                                     The back-extrapolation percent for intravenous dosing based on AUCinf,pred #> 74                                                                                                                                                                                                                                                                      Percent of the AUCinf that is extrapolated after Tlast calculated from the observed Clast #> 75                                                                                                                                                                                                                                                                     Percent of the AUCinf that is extrapolated after Tlast calculated from the predicted Clast #> 76                                                                                                                                                                                                                                                                                            Clearance or observed oral clearance calculated with observed Clast #> 77                                                                                                                                                                                                                                                                                           Clearance or observed oral clearance calculated with predicted Clast #> 78                                                                                                                                                                                                                                                                                                       The mean residence time to infinity using observed Clast #> 79                                                                                                                                                                                                                                                                                                      The mean residence time to infinity using predicted Clast #> 80                                                                                                                                                                                                                                                                        The mean residence time to infinity using observed Clast correcting for dosing duration #> 81                                                                                                                                                                                                                                                                       The mean residence time to infinity using predicted Clast correcting for dosing duration #> 82                                                                                                                                                                                                                                                                       The mean residence time with multiple dosing and nonlinear kinetics using observed Clast #> 83                                                                                                                                                                                                                                                                      The mean residence time with multiple dosing and nonlinear kinetics using predicted Clast #> 84                                                                                                                                                                                                                                                                                                       The terminal volume of distribution using observed Clast #> 85                                                                                                                                                                                                                                                                                                      The terminal volume of distribution using predicted Clast #> 86                                                                                                                                                                                                                                                                                                   The steady-state volume of distribution using observed Clast #> 87                                                                                                                                                                                                                                                                                                  The steady-state volume of distribution using predicted Clast #> 88                                                                                                                                                                                                                                                                         The steady-state volume of distribution with intravenous infusion using observed Clast #> 89                                                                                                                                                                                                                                                                        The steady-state volume of distribution with intravenous infusion using predicted Clast #> 90                                                                                                                                                                                                                                                                  The steady-state volume of distribution for nonlinear multiple-dose data using observed Clast #> 91                                                                                                                                                                                                                                                                 The steady-state volume of distribution for nonlinear multiple-dose data using predicted Clast #> 92                                                                                                                                                                                                                                                                                        Apparent observed volume of distribution calculated with observed Clast #> 93                                                                                                                                                                                                                                                                                       Apparent observed volume of distribution calculated with predicted Clast #> 94                                                                                                                                                                                                                                                                                                        The effective half-life (as determined from the MRTobs) #> 95                                                                                                                                                                                                                                                                                                       The effective half-life (as determined from the MRTpred) #> 96                                                                                                                                                                                                                                                                                            The effective half-life (as determined from the intravenous MRTobs) #> 97                                                                                                                                                                                                                                                                                           The effective half-life (as determined from the intravenous MRTpred) #> 98                                                                                                                                                                                                                                                                                              Elimination rate (as calculated from the MRT with observed Clast) #> 99                                                                                                                                                                                                                                                                                             Elimination rate (as calculated from the MRT with predicted Clast) #> 100                                                                                                                                                                                                                                                                                                  Elimination rate (as calculated from the intravenous MRTobs) #> 101                                                                                                                                                                                                                                                                                                 Elimination rate (as calculated from the intravenous MRTpred) #> 102                                                                                                                                                                                                                                                                                                                                       Dose normalized auclast #> 103                                                                                                                                                                                                                                                                                                                                        Dose normalized aucall #> 104                                                                                                                                                                                                                                                                                                                                    Dose normalized aucinf.obs #> 105                                                                                                                                                                                                                                                                                                                                   Dose normalized aucinf.pred #> 106                                                                                                                                                                                                                                                                                                                                      Dose normalized aumclast #> 107                                                                                                                                                                                                                                                                                                                                       Dose normalized aumcall #> 108                                                                                                                                                                                                                                                                                                                                   Dose normalized aumcinf.obs #> 109                                                                                                                                                                                                                                                                                                                                  Dose normalized aumcinf.pred #> 110                                                                                                                                                                                                                                                                                                                                          Dose normalized cmax #> 111                                                                                                                                                                                                                                                                                                                                          Dose normalized cmin #> 112                                                                                                                                                                                                                                                                                                                                     Dose normalized clast.obs #> 113                                                                                                                                                                                                                                                                                                                                    Dose normalized clast.pred #> 114                                                                                                                                                                                                                                                                                                                                           Dose normalized cav #> 115                                                                                                                                                                                                                                                                                                                                       Dose normalized ctrough #>"},{"path":"/reference/NCA_fetch_ana_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Analysis Dataset — NCA_fetch_ana_ds","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"Fetches dataset used specified analysis","code":""},{"path":"/reference/NCA_fetch_ana_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"","code":"NCA_fetch_ana_ds(state, current_ana)"},{"path":"/reference/NCA_fetch_ana_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"state NCA state NCA_fetch_state() current_ana Current value analysis","code":""},{"path":"/reference/NCA_fetch_ana_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"Dataset ds field FM_fetch_ds()","code":""},{"path":"/reference/NCA_fetch_ana_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Analysis Dataset — NCA_fetch_ana_ds","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:acfc12d32b985885f6ab52a541d0649e                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_ana_pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"Fetches PKNCA output specified analysis","code":""},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"","code":"NCA_fetch_ana_pknca(state, current_ana)"},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"state NCA state NCA_fetch_state() current_ana Current value analysis","code":""},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"Dataset ds field FM_fetch_ds()","code":""},{"path":"/reference/NCA_fetch_ana_pknca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch PKNCA Results Object — NCA_fetch_ana_pknca","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:3c072c7957f8861e121dbbe392490d5e                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_code.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Code — NCA_fetch_code","title":"Fetch Module Code — NCA_fetch_code","text":"Fetches code generate results seen app","code":""},{"path":"/reference/NCA_fetch_code.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Code — NCA_fetch_code","text":"","code":"NCA_fetch_code(state)"},{"path":"/reference/NCA_fetch_code.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Code — NCA_fetch_code","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_fetch_code.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Code — NCA_fetch_code","text":"Character object vector lines code","code":""},{"path":"/reference/NCA_fetch_code.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Code — NCA_fetch_code","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:c46e0a9de29bb71e3b5200e558d94854                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_current_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Current Analysis — NCA_fetch_current_ana","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"Takes NCA state returns current active analysis","code":""},{"path":"/reference/NCA_fetch_current_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"","code":"NCA_fetch_current_ana(state)"},{"path":"/reference/NCA_fetch_current_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_fetch_current_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"List containing details current analysis. structure list structure state$NCA$anas output NCA_fetch_state().","code":""},{"path":"/reference/NCA_fetch_current_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Current Analysis — NCA_fetch_current_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:92aa2dff829b383fb2dfe79a3e1bdbe0                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_fetch_current_obj.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches the Current Analysis Object — NCA_fetch_current_obj","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"Takes current state object type returns currently selected object. example specified figure, look output figure selected figure number figure return ggplot object . subject id highlighting certain NCA aspects (e.g. points used half-life)","code":""},{"path":"/reference/NCA_fetch_current_obj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"","code":"NCA_fetch_current_obj(state, obj_type)"},{"path":"/reference/NCA_fetch_current_obj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"state NCA state NCA_fetch_state() obj_type Type object return (either \"table\" \"figure\").","code":""},{"path":"/reference/NCA_fetch_current_obj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"List format depends obj_type. figures: ggplot:   ggplot object figure. isgood:   Return status function. msgs:     Messages passed back user. tables: df:       Dataframe current table. ft:       Flextable object current table. notes:    table notes included. isgood:   Return status function. msgs:     Messages passed back user.","code":""},{"path":"/reference/NCA_fetch_current_obj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches the Current Analysis Object — NCA_fetch_current_obj","text":"Time Concentration (ng/mL) ID (day) 1 2 3 0 BLQ BLQ BLQ 1 690 750 670 2 588 577 576 3 506 508 501 4 438 476 441 7 303 425 320 21 123 274 139 28 95.5 220 104 35 75.8 177 77.8 42 60.6 142 58.6","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:5d866f91b324cf47f621c05e3b35df20 state = sess_res$state  # Current active table: res = NCA_fetch_current_obj(state, \"table\") res$ft .cl-9075793c{}.cl-90720266{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-90736700{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-907372ea{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-907372f4{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-907372fe{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-907372ff{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737300{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737308{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737309{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9073730a{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737312{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737313{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-90737314{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9073731c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}TimeConcentration (ng/mL) by ID(day)1230BLQBLQBLQ16907506702588577576350650850144384764417303425320211232741392895.52201043575.817777.84260.614258.6 # Current active figure: res = NCA_fetch_current_obj(state, \"figure\") res$ggplot"},{"path":"/reference/NCA_fetch_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Details About Data Requirements — NCA_fetch_data_format","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"Use get information data formats.","code":""},{"path":"/reference/NCA_fetch_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"","code":"NCA_fetch_data_format(   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\") )"},{"path":"/reference/NCA_fetch_data_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"MOD_yaml_file Module configuration file MC main section.","code":""},{"path":"/reference/NCA_fetch_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"List details data formats","code":""},{"path":"/reference/NCA_fetch_data_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Details About Data Requirements — NCA_fetch_data_format","text":"","code":"NCA_fetch_data_format() #>   sname              Column Required #> 1    id          Subject ID      yes #> 2  time                Time      yes #> 3 ntime        Nominal Time      yes #> 4  conc       Concentration      yes #> 5  dose                Dose      yes #> 6 route               Route      yes #> 7   dur Duration (optional)       no #> 8 cycle          Dose cycle      yes #>                                                                                                                          Description #> 1                                                                                                         Unique subject identifier. #> 2                                                                                        Time since the first dose. Must be numeric. #> 3                                                       Time since the last dose in the cycle (same units as Time). Must be numeric. #> 4                                          Observed concentration. Must be numeric, no NA values. Missing values should be set to 0. #> 5                                                                                             Value of dose given (must be numeric). #> 6 Doseing route. It should be either 'extravascular' or 'intravascular' but ruminate will make an effort to figure out other values. #> 7                                                 Doseing duration (same units as Time). If set to N/A a value of 0 will be assumed. #> 8                                                                                                                 Current dose cycle"},{"path":"/reference/NCA_fetch_ds.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch Module Datasets — NCA_fetch_ds","title":"Fetch Module Datasets — NCA_fetch_ds","text":"Fetches datasets contained module","code":""},{"path":"/reference/NCA_fetch_ds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch Module Datasets — NCA_fetch_ds","text":"","code":"NCA_fetch_ds(state)"},{"path":"/reference/NCA_fetch_ds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch Module Datasets — NCA_fetch_ds","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_fetch_ds.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch Module Datasets — NCA_fetch_ds","text":"list containing following elements isgood:    Return status function. hasds:     Boolean indicator module datasets msgs:      Messages passed back user. ds:        List datasets. list element name R-object dataset. element following structure: label: Text label dataset MOD_TYPE: Short name type module. id: module ID DS: Dataframe containing actual dataset. DSMETA: Metadata describing DS code: Complete code build dataset. checksum: Module checksum. DSchecksum: Dataset checksum.","code":""},{"path":"/reference/NCA_fetch_ds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch Module Datasets — NCA_fetch_ds","text":"","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:52d5a2f8439af77e5e911817c5c4ec44 state = sess_res$state  myDs = NCA_fetch_ds(state)"},{"path":"/reference/NCA_fetch_np_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"provides meta information NCA parameters. includes parameter names, text descriptions, formatting (md LaTeX).","code":""},{"path":"/reference/NCA_fetch_np_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"","code":"NCA_fetch_np_meta(   MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\") )"},{"path":"/reference/NCA_fetch_np_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"MOD_yaml_file Module configuration file MC main section.","code":""},{"path":"/reference/NCA_fetch_np_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"List following elements: choices: List parameter choices grouped values specified module configuration file. summary: Data frame meta data NCA parameters following columns: parameter:   Name parameter PKNCA. text:        Name parameter plain text. md:          Parameter name formatted Markdown. latex:       Parameter name formatted using LaTeX. description: Verbose description plain text parameter.","code":""},{"path":"/reference/NCA_fetch_np_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches NCA Parameter Meta Information — NCA_fetch_np_meta","text":"","code":"NCA_fetch_np_meta() #> $choices #> $choices$`Common Parameters` #> $choices$`Common Parameters`$AUCall #> [1] \"aucall\" #>  #> $choices$`Common Parameters`$C0 #> [1] \"c0\" #>  #> $choices$`Common Parameters`$`CL (based on AUCall)` #> [1] \"cl.all\" #>  #> $choices$`Common Parameters`$`CL (based on AUClast)` #> [1] \"cl.last\" #>  #> $choices$`Common Parameters`$Cmax #> [1] \"cmax\" #>  #> $choices$`Common Parameters`$`Half-life` #> [1] \"half.life\" #>  #> $choices$`Common Parameters`$`r²` #> [1] \"r.squared\" #>  #> $choices$`Common Parameters`$`Sparse AUClast` #> [1] \"sparse_auclast\" #>  #>  #> $choices$Other #> $choices$Other$`r² (Adj)` #> [1] \"adj.r.squared\" #>  #> $choices$Other$`Amount excreted` #> [1] \"ae\" #>  #> $choices$Other$`AUCall (dose-normalized)` #> [1] \"aucall.dn\" #>  #> $choices$Other$`AUCinf,obs` #> [1] \"aucinf.obs\" #>  #> $choices$Other$`AUCinf,obs (dose-normalized)` #> [1] \"aucinf.obs.dn\" #>  #> $choices$Other$`AUCinf,pred (dose-normalized)` #> [1] \"aucinf.pred.dn\" #>  #> $choices$Other$`AUCint (based on AUCall extrapolation)` #> [1] \"aucint.all\" #>  #> $choices$Other$`AUCint (based on AUCall extrapolation, dose-aware)` #> [1] \"aucint.all.dose\" #>  #> $choices$Other$`AUCint (based on AUCinf,obs extrapolation)` #> [1] \"aucint.inf.obs\" #>  #> $choices$Other$`AUCint (based on AUCinf,obs extrapolation, dose-aware)` #> [1] \"aucint.inf.obs.dose\" #>  #> $choices$Other$`AUCint (based on AUCinf,pred extrapolation)` #> [1] \"aucint.inf.pred\" #>  #> $choices$Other$`AUCint (based on AUCinf,pred extrapolation, dose-aware)` #> [1] \"aucint.inf.pred.dose\" #>  #> $choices$Other$`AUCint (based on AUClast extrapolation)` #> [1] \"aucint.last\" #>  #> $choices$Other$`AUCint (based on AUClast extrapolation, dose-aware)` #> [1] \"aucint.last.dose\" #>  #> $choices$Other$`AUCall (IV dosing)` #> [1] \"aucivall\" #>  #> $choices$Other$`AUCinf,obs (IV dosing)` #> [1] \"aucivinf.obs\" #>  #> $choices$Other$`AUCinf,pred (IV dosing)` #> [1] \"aucivinf.pred\" #>  #> $choices$Other$`AUCint,all (IV dosing)` #> [1] \"aucivint.all\" #>  #> $choices$Other$`AUCint,last (IV dosing)` #> [1] \"aucivint.last\" #>  #> $choices$Other$`AUClast (IV dosing)` #> [1] \"aucivlast\" #>  #> $choices$Other$`AUCbext (based on AUCall)` #> [1] \"aucivpbextall\" #>  #> $choices$Other$`AUCbext (based on AUCinf,obs)` #> [1] \"aucivpbextinf.obs\" #>  #> $choices$Other$`AUCbext (based on AUCinf,pred)` #> [1] \"aucivpbextinf.pred\" #>  #> $choices$Other$`AUCbext (based on AUCint,all)` #> [1] \"aucivpbextint.all\" #>  #> $choices$Other$`AUCbext (based on AUCint,last)` #> [1] \"aucivpbextint.last\" #>  #> $choices$Other$`AUCbext (based on AUClast)` #> [1] \"aucivpbextlast\" #>  #> $choices$Other$AUClast #> [1] \"auclast\" #>  #> $choices$Other$`AUClast (dose-normalized)` #> [1] \"auclast.dn\" #>  #> $choices$Other$`AUCpext (based on AUCinf,obs)` #> [1] \"aucpext.obs\" #>  #> $choices$Other$`AUCpext (based on AUCinf,pred)` #> [1] \"aucpext.pred\" #>  #> $choices$Other$`AUMC,all` #> [1] \"aumcall\" #>  #> $choices$Other$`AUMC,all (dose-normalized)` #> [1] \"aumcall.dn\" #>  #> $choices$Other$`AUMC,inf,obs` #> [1] \"aumcinf.obs\" #>  #> $choices$Other$`AUMC,inf,obs (dose-normalized)` #> [1] \"aumcinf.obs.dn\" #>  #> $choices$Other$`AUMC,inf,pred (dose-normalized)` #> [1] \"aumcinf.pred.dn\" #>  #> $choices$Other$`AUMC,last` #> [1] \"aumclast\" #>  #> $choices$Other$`AUMC,last (dose-normalized)` #> [1] \"aumclast.dn\" #>  #> $choices$Other$Cav #> [1] \"cav\" #>  #> $choices$Other$`Cav (dose-normalized)` #> [1] \"cav.dn\" #>  #> $choices$Other$Ceoi #> [1] \"ceoi\" #>  #> $choices$Other$`CL (based on AUCinf,obs)` #> [1] \"cl.obs\" #>  #> $choices$Other$`CL (based on AUCinf,pred)` #> [1] \"cl.pred\" #>  #> $choices$Other$Clast #> [1] \"clast.obs\" #>  #> $choices$Other$`Clast,pred` #> [1] \"clast.pred\" #>  #> $choices$Other$`Clast,pred (dose-normalized)` #> [1] \"clast.pred.dn\" #>  #> $choices$Other$`Renal clearance (from AUClast)` #> [1] \"clr.last\" #>  #> $choices$Other$`Renal clearance (from AUCinf,obs)` #> [1] \"clr.obs\" #>  #> $choices$Other$`Renal clearance (from AUCinf,pred)` #> [1] \"clr.pred\" #>  #> $choices$Other$`Cmax (dose-normalized)` #> [1] \"cmax.dn\" #>  #> $choices$Other$Cmin #> [1] \"cmin\" #>  #> $choices$Other$`Cmin (dose-normalized)` #> [1] \"cmin.dn\" #>  #> $choices$Other$Ctrough #> [1] \"ctrough\" #>  #> $choices$Other$`Ctrough (dose-normalized)` #> [1] \"ctrough.dn\" #>  #> $choices$Other$`Degree of fluctuation` #> [1] \"deg.fluc\" #>  #> $choices$Other$`Interval End` #> [1] \"end\" #>  #> $choices$Other$Bioavailability #> [1] \"f\" #>  #> $choices$Other$`Fraction excreted` #> [1] \"fe\" #>  #> $choices$Other$`Kel (for IV dosing, based on AUClast)` #> [1] \"kel.iv.last\" #>  #> $choices$Other$`Kel (for IV dosing, based on AUCinf,obs)` #> [1] \"kel.iv.obs\" #>  #> $choices$Other$`Kel (for IV dosing, based on AUCinf,pred)` #> [1] \"kel.iv.pred\" #>  #> $choices$Other$`Kel (based on AUClast)` #> [1] \"kel.last\" #>  #> $choices$Other$`Kel (based on AUCinf,obs)` #> [1] \"kel.obs\" #>  #> $choices$Other$`Kel (based on AUCinf,pred)` #> [1] \"kel.pred\" #>  #> $choices$Other$λz #> [1] \"lambda.z\" #>  #> $choices$Other$`λz,n` #> [1] \"lambda.z.n.points\" #>  #> $choices$Other$`λz (t 1ˢᵗ)` #> [1] \"lambda.z.time.first\" #>  #> $choices$Other$`MRT (for IV dosing, based on AUClast)` #> [1] \"mrt.iv.last\" #>  #> $choices$Other$`MRT (for IV dosing, based on AUCinf,obs)` #> [1] \"mrt.iv.obs\" #>  #> $choices$Other$`MRT (for IV dosing, based on AUCinf,pred)` #> [1] \"mrt.iv.pred\" #>  #> $choices$Other$`MRT (based on AUClast)` #> [1] \"mrt.last\" #>  #> $choices$Other$`MRT (for multiple dosing, based on AUCinf,obs)` #> [1] \"mrt.md.obs\" #>  #> $choices$Other$`MRT (for multiple dosing, based on AUCinf,pred)` #> [1] \"mrt.md.pred\" #>  #> $choices$Other$`MRT (based on AUCinf,obs)` #> [1] \"mrt.obs\" #>  #> $choices$Other$`MRT (based on AUCinf,pred)` #> [1] \"mrt.pred\" #>  #> $choices$Other$`Peak-to-trough ratio` #> [1] \"ptr\" #>  #> $choices$Other$`Span ratio` #> [1] \"span.ratio\" #>  #> $choices$Other$`Interval Start` #> [1] \"start\" #>  #> $choices$Other$Swing #> [1] \"swing\" #>  #> $choices$Other$`t 1ˢᵗ` #> [1] \"tfirst\" #>  #> $choices$Other$`Effective half-life (for IV dosing, based on MRTlast)` #> [1] \"thalf.eff.iv.last\" #>  #> $choices$Other$`Effective half-life (for IV dosing, based on MRT,obs)` #> [1] \"thalf.eff.iv.obs\" #>  #> $choices$Other$`Effective half-life (for IV dosing, based on MRT,pred)` #> [1] \"thalf.eff.iv.pred\" #>  #> $choices$Other$`Effective half-life (based on MRT,last)` #> [1] \"thalf.eff.last\" #>  #> $choices$Other$`Effective half-life (based on MRT,obs)` #> [1] \"thalf.eff.obs\" #>  #> $choices$Other$`Effective half-life (based on MRT,pred)` #> [1] \"thalf.eff.pred\" #>  #> $choices$Other$`Time above Concentration` #> [1] \"time_above\" #>  #> $choices$Other$Tlag #> [1] \"tlag\" #>  #> $choices$Other$Tlast #> [1] \"tlast\" #>  #> $choices$Other$`Vd (based on Clast,obs)` #> [1] \"vd.obs\" #>  #> $choices$Other$`Vd (based on Clast,pred)` #> [1] \"vd.pred\" #>  #> $choices$Other$`Vss (for IV dosing, based on AUClast)` #> [1] \"vss.iv.last\" #>  #> $choices$Other$`Vss (for IV dosing, based on AUCinf,obs)` #> [1] \"vss.iv.obs\" #>  #> $choices$Other$`Vss (for IV dosing, based on AUCinf,pred)` #> [1] \"vss.iv.pred\" #>  #> $choices$Other$`Vss (based on AUClast)` #> [1] \"vss.last\" #>  #> $choices$Other$`Vss (for multiple-dose, based on Clast,obs)` #> [1] \"vss.md.obs\" #>  #> $choices$Other$`Vss (for multiple-dose, based on Clast,pred)` #> [1] \"vss.md.pred\" #>  #> $choices$Other$`Vss (based on AUCinf,obs)` #> [1] \"vss.obs\" #>  #> $choices$Other$`Vss (based on AUCinf,pred)` #> [1] \"vss.pred\" #>  #> $choices$Other$`Vz (based on AUCinf,obs)` #> [1] \"vz.obs\" #>  #> $choices$Other$`Vz (based on AUCinf,pred)` #> [1] \"vz.pred\" #>  #>  #>  #> $summary #>                parameter #> 1                 aucall #> 2          adj.r.squared #> 3                     ae #> 4              aucall.dn #> 5             aucinf.obs #> 6          aucinf.obs.dn #> 7         aucinf.pred.dn #> 8             aucint.all #> 9        aucint.all.dose #> 10        aucint.inf.obs #> 11   aucint.inf.obs.dose #> 12       aucint.inf.pred #> 13  aucint.inf.pred.dose #> 14           aucint.last #> 15      aucint.last.dose #> 16              aucivall #> 17          aucivinf.obs #> 18         aucivinf.pred #> 19          aucivint.all #> 20         aucivint.last #> 21             aucivlast #> 22         aucivpbextall #> 23     aucivpbextinf.obs #> 24    aucivpbextinf.pred #> 25     aucivpbextint.all #> 26    aucivpbextint.last #> 27        aucivpbextlast #> 28               auclast #> 29            auclast.dn #> 30           aucpext.obs #> 31          aucpext.pred #> 32               aumcall #> 33            aumcall.dn #> 34           aumcinf.obs #> 35        aumcinf.obs.dn #> 36       aumcinf.pred.dn #> 37              aumclast #> 38           aumclast.dn #> 39                    c0 #> 40                   cav #> 41                cav.dn #> 42                  ceoi #> 43                cl.all #> 44               cl.last #> 45                cl.obs #> 46               cl.pred #> 47             clast.obs #> 48            clast.pred #> 49         clast.pred.dn #> 50              clr.last #> 51               clr.obs #> 52              clr.pred #> 53                  cmax #> 54               cmax.dn #> 55                  cmin #> 56               cmin.dn #> 57               ctrough #> 58            ctrough.dn #> 59              deg.fluc #> 60                   end #> 61                     f #> 62                    fe #> 63             half.life #> 64           kel.iv.last #> 65            kel.iv.obs #> 66           kel.iv.pred #> 67              kel.last #> 68               kel.obs #> 69              kel.pred #> 70              lambda.z #> 71     lambda.z.n.points #> 72   lambda.z.time.first #> 73           mrt.iv.last #> 74            mrt.iv.obs #> 75           mrt.iv.pred #> 76              mrt.last #> 77            mrt.md.obs #> 78           mrt.md.pred #> 79               mrt.obs #> 80              mrt.pred #> 81                   ptr #> 82             r.squared #> 83            span.ratio #> 84        sparse_auclast #> 85                 start #> 86                 swing #> 87                tfirst #> 88     thalf.eff.iv.last #> 89      thalf.eff.iv.obs #> 90     thalf.eff.iv.pred #> 91        thalf.eff.last #> 92         thalf.eff.obs #> 93        thalf.eff.pred #> 94            time_above #> 95                  tlag #> 96                 tlast #> 97                vd.obs #> 98               vd.pred #> 99           vss.iv.last #> 100           vss.iv.obs #> 101          vss.iv.pred #> 102             vss.last #> 103           vss.md.obs #> 104          vss.md.pred #> 105              vss.obs #> 106             vss.pred #> 107               vz.obs #> 108              vz.pred #>                                                        text #> 1                                                    AUCall #> 2                                                  r² (Adj) #> 3                                           Amount excreted #> 4                                  AUCall (dose-normalized) #> 5                                                AUCinf,obs #> 6                              AUCinf,obs (dose-normalized) #> 7                             AUCinf,pred (dose-normalized) #> 8                    AUCint (based on AUCall extrapolation) #> 9        AUCint (based on AUCall extrapolation, dose-aware) #> 10               AUCint (based on AUCinf,obs extrapolation) #> 11   AUCint (based on AUCinf,obs extrapolation, dose-aware) #> 12              AUCint (based on AUCinf,pred extrapolation) #> 13  AUCint (based on AUCinf,pred extrapolation, dose-aware) #> 14                  AUCint (based on AUClast extrapolation) #> 15      AUCint (based on AUClast extrapolation, dose-aware) #> 16                                       AUCall (IV dosing) #> 17                                   AUCinf,obs (IV dosing) #> 18                                  AUCinf,pred (IV dosing) #> 19                                   AUCint,all (IV dosing) #> 20                                  AUCint,last (IV dosing) #> 21                                      AUClast (IV dosing) #> 22                                AUCbext (based on AUCall) #> 23                            AUCbext (based on AUCinf,obs) #> 24                           AUCbext (based on AUCinf,pred) #> 25                            AUCbext (based on AUCint,all) #> 26                           AUCbext (based on AUCint,last) #> 27                               AUCbext (based on AUClast) #> 28                                                  AUClast #> 29                                AUClast (dose-normalized) #> 30                            AUCpext (based on AUCinf,obs) #> 31                           AUCpext (based on AUCinf,pred) #> 32                                                 AUMC,all #> 33                               AUMC,all (dose-normalized) #> 34                                             AUMC,inf,obs #> 35                           AUMC,inf,obs (dose-normalized) #> 36                          AUMC,inf,pred (dose-normalized) #> 37                                                AUMC,last #> 38                              AUMC,last (dose-normalized) #> 39                                                       C0 #> 40                                                      Cav #> 41                                    Cav (dose-normalized) #> 42                                                     Ceoi #> 43                                     CL (based on AUCall) #> 44                                    CL (based on AUClast) #> 45                                 CL (based on AUCinf,obs) #> 46                                CL (based on AUCinf,pred) #> 47                                                    Clast #> 48                                               Clast,pred #> 49                             Clast,pred (dose-normalized) #> 50                           Renal clearance (from AUClast) #> 51                        Renal clearance (from AUCinf,obs) #> 52                       Renal clearance (from AUCinf,pred) #> 53                                                     Cmax #> 54                                   Cmax (dose-normalized) #> 55                                                     Cmin #> 56                                   Cmin (dose-normalized) #> 57                                                  Ctrough #> 58                                Ctrough (dose-normalized) #> 59                                    Degree of fluctuation #> 60                                             Interval End #> 61                                          Bioavailability #> 62                                        Fraction excreted #> 63                                                Half-life #> 64                    Kel (for IV dosing, based on AUClast) #> 65                 Kel (for IV dosing, based on AUCinf,obs) #> 66                Kel (for IV dosing, based on AUCinf,pred) #> 67                                   Kel (based on AUClast) #> 68                                Kel (based on AUCinf,obs) #> 69                               Kel (based on AUCinf,pred) #> 70                                                       λz #> 71                                                     λz,n #> 72                                               λz (t 1ˢᵗ) #> 73                    MRT (for IV dosing, based on AUClast) #> 74                 MRT (for IV dosing, based on AUCinf,obs) #> 75                MRT (for IV dosing, based on AUCinf,pred) #> 76                                   MRT (based on AUClast) #> 77           MRT (for multiple dosing, based on AUCinf,obs) #> 78          MRT (for multiple dosing, based on AUCinf,pred) #> 79                                MRT (based on AUCinf,obs) #> 80                               MRT (based on AUCinf,pred) #> 81                                     Peak-to-trough ratio #> 82                                                       r² #> 83                                               Span ratio #> 84                                           Sparse AUClast #> 85                                           Interval Start #> 86                                                    Swing #> 87                                                    t 1ˢᵗ #> 88    Effective half-life (for IV dosing, based on MRTlast) #> 89    Effective half-life (for IV dosing, based on MRT,obs) #> 90   Effective half-life (for IV dosing, based on MRT,pred) #> 91                  Effective half-life (based on MRT,last) #> 92                   Effective half-life (based on MRT,obs) #> 93                  Effective half-life (based on MRT,pred) #> 94                                 Time above Concentration #> 95                                                     Tlag #> 96                                                    Tlast #> 97                                  Vd (based on Clast,obs) #> 98                                 Vd (based on Clast,pred) #> 99                    Vss (for IV dosing, based on AUClast) #> 100                Vss (for IV dosing, based on AUCinf,obs) #> 101               Vss (for IV dosing, based on AUCinf,pred) #> 102                                  Vss (based on AUClast) #> 103             Vss (for multiple-dose, based on Clast,obs) #> 104            Vss (for multiple-dose, based on Clast,pred) #> 105                               Vss (based on AUCinf,obs) #> 106                              Vss (based on AUCinf,pred) #> 107                                Vz (based on AUCinf,obs) #> 108                               Vz (based on AUCinf,pred) #>                                 md #> 1                         AUC~all~ #> 2                        Adj. R^2^ #> 3                               AE #> 4                      AUC~all,dn~ #> 5                     AUC~inf,obs~ #> 6                  AUC~inf,obs,dn~ #> 7                 AUC~inf,pred,dn~ #> 8                     AUC~int,all~ #> 9                AUC~int,all,dose~ #> 10                AUC~int,inf,obs~ #> 11           AUC~int,inf,obs,dose~ #> 12               AUC~int,inf,pred~ #> 13          AUC~int,inf,pred,dose~ #> 14                   AUC~int,last~ #> 15              AUC~int,last,dose~ #> 16                     AUC~iv,all~ #> 17                 AUC~iv,inf,obs~ #> 18                AUC~iv,inf,pred~ #> 19                 AUC~iv,int,all~ #> 20                AUC~iv,int,last~ #> 21                    AUC~iv,last~ #> 22                 AUC~iv,pbe,all~ #> 23             AUC~iv,pbe,inf,obs~ #> 24            AUC~iv,pbe,inf,pred~ #> 25             AUC~iv,pbe,inf,all~ #> 26            AUC~iv,pbe,inf,last~ #> 27                AUC~iv,pbe,last~ #> 28                       AUC~last~ #> 29                    AUC~last,dn~ #> 30                   AUC~pext,obs~ #> 31                  AUC~pext,pred~ #> 32                       AUMC~all~ #> 33                    AUMC~all,dn~ #> 34                   AUMC~inf,obs~ #> 35                AUMC~inf,obs,dn~ #> 36               AUMC~inf,pred,dn~ #> 37                      AUMC~last~ #> 38                   AUMC~last,dn~ #> 39                            C~0~ #> 40                          C~ave~ #> 41                       C~ave,dn~ #> 42                          C~EOI~ #> 43                              CL #> 44                        CL~last~ #> 45                         CL~obs~ #> 46                        CL~pred~ #> 47                    CL~last,obs~ #> 48                   CL~last,pred~ #> 49                CL~last,pred,dn~ #> 50                      CL~R,last~ #> 51                       CL~R,obs~ #> 52                      CL~R,pred~ #> 53                          C~max~ #> 54                       C~max,dn~ #> 55                          C~min~ #> 56                       C~min,dn~ #> 57                           C~tr~ #> 58                        C~tr,dn~ #> 59                             dgf #> 60                          t~end~ #> 61                            f~b~ #> 62                            f~e~ #> 63                         t~half~ #> 64                   k~el,iv,last~ #> 65                    k~el,iv,obs~ #> 66                   k~el,iv,pred~ #> 67                      k~el,last~ #> 68                       k~el,obs~ #> 69                      k~el,pred~ #> 70            <ff:symbol>l<\/ff>~z~ #> 71          <ff:symbol>l<\/ff>~z,n~ #> 72  <ff:symbol>l<\/ff>~z~ (t 1^st^) #> 73                    MRT~iv,last~ #> 74                     MRT~iv,obs~ #> 75                    MRT~iv,pred~ #> 76                       MRT~last~ #> 77                     MRT~md,obs~ #> 78                    MRT~md,pred~ #> 79                        MRT~obs~ #> 80                       MRT~pred~ #> 81                             PTR #> 82                            R^2^ #> 83                              SR #> 84                    AUC~last,sp~ #> 85                        t~start~ #> 86                           SWING #> 87                        t~first~ #> 88             t~half,eff,iv,last~ #> 89              t~half,eff,iv,obs~ #> 90             t~half,eff,iv,pred~ #> 91                t~half,eff,last~ #> 92                 t~half,eff,obs~ #> 93                t~half,eff,pred~ #> 94                           t~ab~ #> 95                          t~lag~ #> 96                         t~last~ #> 97                        V~d,obs~ #> 98                       V~d,pred~ #> 99                   V~ss,iv,last~ #> 100                   V~ss,iv,obs~ #> 101                  V~ss,iv,pred~ #> 102                  V~ss,iv,last~ #> 103                   V~ss,md,obs~ #> 104                  V~ss,md,pred~ #> 105                      V~ss,obs~ #> 106                     V~ss,pred~ #> 107                       V~z,obs~ #> 108                      V~z,pred~ #>                                                       latex #> 1                                                    AUCall #> 2                                               $r^2_{adj}$ #> 3                                           Amount excreted #> 4                                  AUCall (dose-normalized) #> 5                                                AUCinf,obs #> 6                              AUCinf,obs (dose-normalized) #> 7                             AUCinf,pred (dose-normalized) #> 8                    AUCint (based on AUCall extrapolation) #> 9        AUCint (based on AUCall extrapolation, dose-aware) #> 10               AUCint (based on AUCinf,obs extrapolation) #> 11   AUCint (based on AUCinf,obs extrapolation, dose-aware) #> 12              AUCint (based on AUCinf,pred extrapolation) #> 13  AUCint (based on AUCinf,pred extrapolation, dose-aware) #> 14                  AUCint (based on AUClast extrapolation) #> 15      AUCint (based on AUClast extrapolation, dose-aware) #> 16                                       AUCall (IV dosing) #> 17                                   AUCinf,obs (IV dosing) #> 18                                  AUCinf,pred (IV dosing) #> 19                                   AUCint,all (IV dosing) #> 20                                  AUCint,last (IV dosing) #> 21                                      AUClast (IV dosing) #> 22                                AUCbext (based on AUCall) #> 23                            AUCbext (based on AUCinf,obs) #> 24                           AUCbext (based on AUCinf,pred) #> 25                            AUCbext (based on AUCint,all) #> 26                           AUCbext (based on AUCint,last) #> 27                               AUCbext (based on AUClast) #> 28                                                  AUClast #> 29                                AUClast (dose-normalized) #> 30                            AUCpext (based on AUCinf,obs) #> 31                           AUCpext (based on AUCinf,pred) #> 32                                                 AUMC,all #> 33                               AUMC,all (dose-normalized) #> 34                                             AUMC,inf,obs #> 35                           AUMC,inf,obs (dose-normalized) #> 36                          AUMC,inf,pred (dose-normalized) #> 37                                                AUMC,last #> 38                              AUMC,last (dose-normalized) #> 39                                                       C0 #> 40                                                      Cav #> 41                                    Cav (dose-normalized) #> 42                                                     Ceoi #> 43                                     CL (based on AUCall) #> 44                                    CL (based on AUClast) #> 45                                 CL (based on AUCinf,obs) #> 46                                CL (based on AUCinf,pred) #> 47                                                    Clast #> 48                                               Clast,pred #> 49                             Clast,pred (dose-normalized) #> 50                           Renal clearance (from AUClast) #> 51                        Renal clearance (from AUCinf,obs) #> 52                       Renal clearance (from AUCinf,pred) #> 53                                                     Cmax #> 54                                   Cmax (dose-normalized) #> 55                                                     Cmin #> 56                                   Cmin (dose-normalized) #> 57                                                  Ctrough #> 58                                Ctrough (dose-normalized) #> 59                                    Degree of fluctuation #> 60                                             Interval End #> 61                                          Bioavailability #> 62                                        Fraction excreted #> 63                                                Half-life #> 64                    Kel (for IV dosing, based on AUClast) #> 65                 Kel (for IV dosing, based on AUCinf,obs) #> 66                Kel (for IV dosing, based on AUCinf,pred) #> 67                                   Kel (based on AUClast) #> 68                                Kel (based on AUCinf,obs) #> 69                               Kel (based on AUCinf,pred) #> 70                                             $\\\\lambda_z$ #> 71                       Number of points used for lambda_z #> 72                              First time for $\\\\lambda_z$ #> 73                    MRT (for IV dosing, based on AUClast) #> 74                 MRT (for IV dosing, based on AUCinf,obs) #> 75                MRT (for IV dosing, based on AUCinf,pred) #> 76                                   MRT (based on AUClast) #> 77           MRT (for multiple dosing, based on AUCinf,obs) #> 78          MRT (for multiple dosing, based on AUCinf,pred) #> 79                                MRT (based on AUCinf,obs) #> 80                               MRT (based on AUCinf,pred) #> 81                                     Peak-to-trough ratio #> 82                                                    $r^2$ #> 83                                               Span ratio #> 84                                           Sparse AUClast #> 85                                           Interval Start #> 86                                                    Swing #> 87                                                   Tfirst #> 88    Effective half-life (for IV dosing, based on MRTlast) #> 89    Effective half-life (for IV dosing, based on MRT,obs) #> 90   Effective half-life (for IV dosing, based on MRT,pred) #> 91                  Effective half-life (based on MRT,last) #> 92                   Effective half-life (based on MRT,obs) #> 93                  Effective half-life (based on MRT,pred) #> 94                                 Time above Concentration #> 95                                                     Tlag #> 96                                                    Tlast #> 97                                  Vd (based on Clast,obs) #> 98                                 Vd (based on Clast,pred) #> 99                    Vss (for IV dosing, based on AUClast) #> 100                Vss (for IV dosing, based on AUCinf,obs) #> 101               Vss (for IV dosing, based on AUCinf,pred) #> 102                                  Vss (based on AUClast) #> 103             Vss (for multiple-dose, based on Clast,obs) #> 104            Vss (for multiple-dose, based on Clast,pred) #> 105                               Vss (based on AUCinf,obs) #> 106                              Vss (based on AUCinf,pred) #> 107                                Vz (based on AUCinf,obs) #> 108                               Vz (based on AUCinf,pred) #>                                                                                                                                                                                                                                                                          description #> 1                        The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification plus the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 2                                                                                                                                                                                                                                The adjusted r^2 value of the half-life calculation #> 3                                                                                                                                                                                                                                The amount excreted (typically into urine or feces) #> 4                                                                                                                                                                                                                                                             Dose normalized aucall #> 5                                                                                                                                  The area under the concentration time curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 6                                                                                                                                                                                                                                                         Dose normalized aucinf.obs #> 7                                                                                                                                                                                                                                                        Dose normalized aucinf.pred #> 8                                                                                              The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 9                                The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 10                                                                                                                                                    The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 11                                                                                      The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 12                                                                                             The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) #> 13                               The area under the concentration time curve in the interval extrapolating from Tlast to infinity with the triangle from Tlast to the next point and zero thereafter (matching AUCall) with dose-aware interpolation/extrapolation of concentrations #> 14                                                                                                                                                    The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) #> 15                                                                                      The area under the concentration time curve in the interval extrapolating from Tlast to infinity with zeros (matching AUClast) with dose-aware interpolation/extrapolation of concentrations #> 16                                                                                                                                                                                        The AUCall calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 17                                                                                                                                                                                    The AUCinf,obs calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 18                                                                                                                                                                                              The  calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 19                                                                                                                                                                                    The AUCint,all calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 20                                                                                                                                                                                   The AUCint,last calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 21                                                                                                                                                                                       The AUClast calculated with back-extrapolation for intravenous dosing using extrapolated C0 #> 22                                                                                                                                                                                                             The back-extrapolation percent for intravenous dosing based on AUCall #> 23                                                                                                                                                                                                         The back-extrapolation percent for intravenous dosing based on AUCinf,obs #> 24                                                                                                                                                                                                        The back-extrapolation percent for intravenous dosing based on AUCinf,pred #> 25                                                                                                                                                                                                         The back-extrapolation percent for intravenous dosing based on AUCint,all #> 26                                                                                                                                                                                                        The back-extrapolation percent for intravenous dosing based on AUCint,last #> 27                                                                                                                                                                                                            The back-extrapolation percent for intravenous dosing based on AUClast #> 28                                                                                                                                        The area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 29                                                                                                                                                                                                                                                           Dose normalized auclast #> 30                                                                                                                                                                                         Percent of the AUCinf that is extrapolated after Tlast calculated from the observed Clast #> 31                                                                                                                                                                                        Percent of the AUCinf that is extrapolated after Tlast calculated from the predicted Clast #> 32  The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification plus the moment of the triangle from that last concentration to 0 at the first concentration below the limit of quantification #> 33                                                                                                                                                                                                                                                           Dose normalized aumcall #> 34                                                                                                                          The area under the concentration time moment curve from the beginning of the interval to infinity with extrapolation to infinity from the observed Clast #> 35                                                                                                                                                                                                                                                       Dose normalized aumcinf.obs #> 36                                                                                                                                                                                                                                                      Dose normalized aumcinf.pred #> 37                                                                                                                                 The area under the concentration time moment curve from the beginning of the interval to the last concentration above the limit of quantification #> 38                                                                                                                                                                                                                                                          Dose normalized aumclast #> 39                                                                                                                                                                                                                                           Initial concentration after an IV bolus #> 40                                                                                                                                                                                                                                      The average concentration during an interval #> 41                                                                                                                                                                                                                                                               Dose normalized cav #> 42                                                                                                                                                                                                                                              Concentration at the end of infusion #> 43                                                                                                                                                                                                                       Clearance or observed oral clearance calculated with AUCall #> 44                                                                                                                                                                                                                          Clearance or observed oral clearance calculated to Clast #> 45                                                                                                                                                                                                               Clearance or observed oral clearance calculated with observed Clast #> 46                                                                                                                                                                                                              Clearance or observed oral clearance calculated with predicted Clast #> 47                                                                                                                                                                                                                 The last concentration observed above the limit of quantification #> 48                                                                                                                                                                                                                          The concentration at Tlast as predicted by the half-life #> 49                                                                                                                                                                                                                                                        Dose normalized clast.pred #> 50                                                                                                                                                                                                                                      The renal clearance calculated using AUClast #> 51                                                                                                                                                                                                                                   The renal clearance calculated using AUCinf,obs #> 52                                                                                                                                                                                                                                  The renal clearance calculated using AUCinf,pred #> 53                                                                                                                                                                                                                                                    Maximum observed concentration #> 54                                                                                                                                                                                                                                                              Dose normalized cmax #> 55                                                                                                                                                                                                                                                    Minimum observed concentration #> 56                                                                                                                                                                                                                                                              Dose normalized cmin #> 57                                                                                                                                                                                                                                        The trough (end of interval) concentration #> 58                                                                                                                                                                                                                                                           Dose normalized ctrough #> 59                                                                                                                                                                                                                                                             Degree of fluctuation #> 60                                                                                                                                                                                                                                Ending time of the interval (potentially infinity) #> 61                                                                                                                                                                                                                                       Bioavailability or relative bioavailability #> 62                                                                                                                                                                                                                                                 The fraction of the dose excreted #> 63                                                                                                                                                                                                                                                          The (terminal) half-life #> 64                                                                                                                                                                                                                     Elimination rate (as calculated from the intravenous MRTlast) #> 65                                                                                                                                                                                                                      Elimination rate (as calculated from the intravenous MRTobs) #> 66                                                                                                                                                                                                                     Elimination rate (as calculated from the intravenous MRTpred) #> 67                                                                                                                                                                                                                       Elimination rate (as calculated from the MRT using AUClast) #> 68                                                                                                                                                                                                                 Elimination rate (as calculated from the MRT with observed Clast) #> 69                                                                                                                                                                                                                Elimination rate (as calculated from the MRT with predicted Clast) #> 70                                                                                                                                                                                                                                    The elimination rate of the terminal half-life #> 71                                                                                                                                                                                                                        The number of points used for the calculation of half-life #> 72                                                                                                                                                                                                                        The first time point used for the calculation of half-life #> 73                                                                                                                                                                           The mean residence time to the last observed concentration above the LOQ correcting for dosing duration #> 74                                                                                                                                                                                           The mean residence time to infinity using observed Clast correcting for dosing duration #> 75                                                                                                                                                                                          The mean residence time to infinity using predicted Clast correcting for dosing duration #> 76                                                                                                                                                                                                          The mean residence time to the last observed concentration above the LOQ #> 77                                                                                                                                                                                          The mean residence time with multiple dosing and nonlinear kinetics using observed Clast #> 78                                                                                                                                                                                         The mean residence time with multiple dosing and nonlinear kinetics using predicted Clast #> 79                                                                                                                                                                                                                          The mean residence time to infinity using observed Clast #> 80                                                                                                                                                                                                                         The mean residence time to infinity using predicted Clast #> 81                                                                                                                                                                                                                                                   Peak-to-Trough ratio (fraction) #> 82                                                                                                                                                                                                                                        The r^2 value of the half-life calculation #> 83                                                                                                                                                                                                         The ratio of the half-life to the duration used for half-life calculation #> 84                                                                                                                For sparse PK sampling, the area under the concentration time curve from the beginning of the interval to the last concentration above the limit of quantification #> 85                                                                                                                                                                                                                                                     Starting time of the interval #> 86                                                                                                                                                                                                                                                            Swing relative to Cmin #> 87                                                                                                                                                                                                                 Time of the first concentration above the limit of quantification #> 88                                                                                                                                                                                                              The effective half-life (as determined from the intravenous MRTlast) #> 89                                                                                                                                                                                                               The effective half-life (as determined from the intravenous MRTobs) #> 90                                                                                                                                                                                                              The effective half-life (as determined from the intravenous MRTpred) #> 91                                                                                                                                                                                                                          The effective half-life (as determined from the MRTlast) #> 92                                                                                                                                                                                                                           The effective half-life (as determined from the MRTobs) #> 93                                                                                                                                                                                                                          The effective half-life (as determined from the MRTpred) #> 94                                                                                                                                                                                                                                                  Time above a given concentration #> 95                                                                                                                                                                                                                                                                          Lag time #> 96                                                                                                                                                                                                         Time of the last concentration observed above the limit of quantification #> 97                                                                                                                                                                                                           Apparent observed volume of distribution calculated with observed Clast #> 98                                                                                                                                                                                                          Apparent observed volume of distribution calculated with predicted Clast #> 99                                                                                                                                                                                       The steady-state volume of distribution with intravenous infusion calculating through Tlast #> 100                                                                                                                                                                                           The steady-state volume of distribution with intravenous infusion using observed Clast #> 101                                                                                                                                                                                          The steady-state volume of distribution with intravenous infusion using predicted Clast #> 102                                                                                                                                                                                                                The steady-state volume of distribution calculating through Tlast #> 103                                                                                                                                                                                    The steady-state volume of distribution for nonlinear multiple-dose data using observed Clast #> 104                                                                                                                                                                                   The steady-state volume of distribution for nonlinear multiple-dose data using predicted Clast #> 105                                                                                                                                                                                                                     The steady-state volume of distribution using observed Clast #> 106                                                                                                                                                                                                                    The steady-state volume of distribution using predicted Clast #> 107                                                                                                                                                                                                                         The terminal volume of distribution using observed Clast #> 108                                                                                                                                                                                                                        The terminal volume of distribution using predicted Clast #>"},{"path":"/reference/NCA_fetch_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch ruminate State — NCA_fetch_state","title":"Fetch ruminate State — NCA_fetch_state","text":"Merges default app options changes made UI","code":""},{"path":"/reference/NCA_fetch_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch ruminate State — NCA_fetch_state","text":"","code":"NCA_fetch_state(   id,   input,   session,   FM_yaml_file,   MOD_yaml_file,   id_ASM,   id_UD,   id_DW,   react_state )"},{"path":"/reference/NCA_fetch_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch ruminate State — NCA_fetch_state","text":"id Shiny module ID input Shiny input variable session Shiny session variable FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id_ASM ID string app state management module used save load app states id_UD ID string upload data module used save load app states id_DW ID string data wrangling module used save load app states react_state Variable passed server allow reaction outside module (NULL)","code":""},{"path":"/reference/NCA_fetch_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch ruminate State — NCA_fetch_state","text":"list containing current state app including default values yaml file well changes made user. list following structure: yaml: Full contents supplied yaml file. MC: Module components yaml file. NCA: ana_cntr:       Analysis counter. anas:                    List analyses: analysis following  structure: ana_dsview:       Dataset view/ID (name DSV) selected data source analysis. ana_scenario:     Analysis scenario selected UI checksum:         checksum analysis (used detect changes analysis). code:             Code generate analysis start finish error messages code generation/analysis failed. code_components:  List containing different components code col_conc:         Column ana_dsview containing concentration data. col_dose:         Column ana_dsview containing dose amount. col_dur:          Column ana_dsview containing infusion duration N/unused. col_group:        Columns ana_dsview containing grouping variables. col_id:           Column ana_dsview containing subject IDs. col_ntime:        Column ana_dsview containing nominal time values col_route:        Column ana_dsview containing dosing route. col_time:         Column ana_dsview containing time values. id:               Character id (ana_idx). idx:              Numeric id (1). include_units:    Boolean variable indicating units included analysis. interval_range:   Vector first element representing beginning interval second element containing end interval. intervals:        List intervals include. isgood:           Current status analysis. key:              Analysis key acts title/caption (user editable) msgs:             Messages generated checking configuration analysis options. nca_config:       List NCA configuration options analysis. nca_object_name:  Prefix NCA objects associated analyis. nca_parameters:   NCA parameters selected calculation UI. notes:            Analysis notes  (user editable) objs:             List names values objects created generated code. sampling:         Sampling method either \"sparse\" \"serial\" units_amt:        Amount units. units_conc:       Concentration units. units_dose:       Dosing units. units_time:       Time units. current_ana:           Currently selected analysis (list name element anas). DSV:                   Available data source views (see FM_fetch_ds) checksum:              MD5 sum module (checksum analysis checksums). nca_config:            List PKNCA configuration options analysis. nca_parameters:        List two elements choices:          List consisting \"Common Parameters\" \"\" (used grouping UI). list text parameter names value PKNCA parameter name. summary:          Summary table following columns: parameter:      PKNCA Paramter name. text:           Name used text output. md:             Name used markdown output. latex:          Name used latex output. description:    Verbose textual description parameter. ui:                    Current value form elements UI. ui_ana_map:            Map UI element names analysis object get NCA_fetch_current_ana ui_ids:                Vector UI elements module. ui_hold:               List hold elements disable updates full ui referesh complete. MOD_TYPE: Character data containing type module \"NCA\" id: Character data containing module id module session variable. FM_yaml_file: App configuration file FM main section. MOD_yaml_file:  Module configuration file MC main section.","code":""},{"path":"/reference/NCA_fetch_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch ruminate State — NCA_fetch_state","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:a5bb5d7b20c96ab03e19eea14dc7dffe                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_find_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Determines Default Column Name — NCA_find_col","title":"Determines Default Column Name — NCA_find_col","text":"Based current analysis, value UI, optional list patterns search, column names dataset function tries find default value column analysis (e.g. subject id, dose, concentration, etc). Generally following done: curr_ui non-NULL, non-\"\" value compared dscols. found value returned. patterns considered. patterns YAML file NULL compared sequentially columns names. first match found returned. nothing found first value dscols returned.","code":""},{"path":"/reference/NCA_find_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determines Default Column Name — NCA_find_col","text":"","code":"NCA_find_col(   curr_ana = NULL,   curr_ui = NULL,   patterns = NULL,   dscols,   null_ok = FALSE )"},{"path":"/reference/NCA_find_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determines Default Column Name — NCA_find_col","text":"curr_ana Current value analysis curr_ui Current value UI patterns List regular expression patterns consider. dscols Columns dataset. null_ok Logical value indicating null result (nothing found) OK (default: FALSE)","code":""},{"path":"/reference/NCA_find_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determines Default Column Name — NCA_find_col","text":"Name column found based rules .","code":""},{"path":"/reference/NCA_find_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determines Default Column Name — NCA_find_col","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:72ab0c616b09e538de2950b1c4b79238                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_init_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize NCA Module State — NCA_init_state","title":"Initialize NCA Module State — NCA_init_state","text":"Creates list initialized module state","code":""},{"path":"/reference/NCA_init_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize NCA Module State — NCA_init_state","text":"","code":"NCA_init_state(FM_yaml_file, MOD_yaml_file, id, id_UD, id_DW, session)"},{"path":"/reference/NCA_init_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize NCA Module State — NCA_init_state","text":"FM_yaml_file App configuration file FM main section. MOD_yaml_file Module configuration file MC main section. id ID string module. id_UD ID string upload data module used handle uploads name list element react_state data set stored. id_DW ID string data wrangling module process uploaded data session Shiny session variable (app) list (outside app)","code":""},{"path":"/reference/NCA_init_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize NCA Module State — NCA_init_state","text":"list containing empty NCA state","code":""},{"path":"/reference/NCA_load_scenario.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads Pre-Defined Scenario — NCA_load_scenario","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"Loads pre-defined analysis scneario NCA YAML config file.","code":""},{"path":"/reference/NCA_load_scenario.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"","code":"NCA_load_scenario(state, ana_scenario)"},{"path":"/reference/NCA_load_scenario.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"state NCA state NCA_fetch_state() ana_scenario Short name analysis scenario load config file.","code":""},{"path":"/reference/NCA_load_scenario.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads Pre-Defined Scenario — NCA_load_scenario","text":"NCA state object scenario loaded relevant notifications set.","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetch PKNCA Results Object — NCA_mkactive_ana","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"Fetches PKNCA output specified analysis","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"","code":"NCA_mkactive_ana(state, ana_id)"},{"path":"/reference/NCA_mkactive_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"state NCA state NCA_fetch_state() ana_id Analysis ID make active.","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"State analysis ID made active. JMH add example script ","code":""},{"path":"/reference/NCA_mkactive_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetch PKNCA Results Object — NCA_mkactive_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:e4497a31c610e31ca6e3073180d6bc1d                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_new_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize New Analysis — NCA_new_ana","title":"Initialize New Analysis — NCA_new_ana","text":"Creates new NCA analysis NCA module","code":""},{"path":"/reference/NCA_new_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize New Analysis — NCA_new_ana","text":"","code":"NCA_new_ana(state)"},{"path":"/reference/NCA_new_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize New Analysis — NCA_new_ana","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_new_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize New Analysis — NCA_new_ana","text":"NCA state object containing new empty analysis analysis set current active analyisis","code":""},{"path":"/reference/NCA_new_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize New Analysis — NCA_new_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:0c11176cc0bc70425aeb3da2bf2a9264                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_process_current_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Processes Current Analysis to be Run — NCA_process_current_ana","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"Takes current analysis checks different aspects issues make sure good go.","code":""},{"path":"/reference/NCA_process_current_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"","code":"NCA_process_current_ana(state)"},{"path":"/reference/NCA_process_current_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"state NCA state NCA_fetch_state()","code":""},{"path":"/reference/NCA_process_current_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"Current analysis list isgood msgs set","code":""},{"path":"/reference/NCA_process_current_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Processes Current Analysis to be Run — NCA_process_current_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:dfc696548b791a5d407726a6bde87421                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_set_current_ana.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets Current Analysis — NCA_set_current_ana","title":"Sets Current Analysis — NCA_set_current_ana","text":"Takes NCA state analysis list sets figure list value active figure","code":""},{"path":"/reference/NCA_set_current_ana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets Current Analysis — NCA_set_current_ana","text":"","code":"NCA_set_current_ana(state, ana)"},{"path":"/reference/NCA_set_current_ana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets Current Analysis — NCA_set_current_ana","text":"state NCA state NCA_fetch_state() ana Analysis list NCA_fetch_current_ana","code":""},{"path":"/reference/NCA_set_current_ana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets Current Analysis — NCA_set_current_ana","text":"State current analysis updated","code":""},{"path":"/reference/NCA_set_current_ana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets Current Analysis — NCA_set_current_ana","text":"","code":"library(ruminate) # Module IDs                                                                    id     = \"NCA\"                                                                  id_UD  = \"UD\"                                                                   id_DW  = \"DW\"                                                                   id_ASM = \"ASM\"                                                                  # We need session and input variables to be define                               sess_res = NCA_test_mksession(session=list(),                                        id     = id,                                                                    id_UD  = id_UD,                                                                 id_DW  = id_DW,                                                                 id_ASM = id_ASM,       full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:5baf846cbf57819d7602dcc87f9499d2                                                                                 # Extracting the session and input variables                                    session      = sess_res$session                                                 input        = sess_res$input                                                   react_state  = list()                                                                                                                                           # We also need configuration files                                              FM_yaml_file  = system.file(package = \"formods\",  \"templates\", \"formods.yaml\")  MOD_yaml_file = system.file(package = \"ruminate\", \"templates\", \"NCA.yaml\")                                                                                      # Getting the current module state                                              state = NCA_fetch_state(id             = id,                                                           input           = input,                                                        session         = session,                                                      FM_yaml_file    = FM_yaml_file,                                                 MOD_yaml_file   = MOD_yaml_file,                                                id_ASM          = id_ASM,                                                       id_UD           = id_UD,                                                        id_DW           = id_DW,                                                        react_state     = react_state)                                                                                                           # Pulls out the active analysis                                                 current_ana = NCA_fetch_current_ana(state)  # This will get the dataset associated with this analysis ds = NCA_fetch_ana_ds(state, current_ana)  # After making changes you can update those in the state state = NCA_set_current_ana(state, current_ana)  # You can use this to check the current analysis current_ana = NCA_process_current_ana(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion  # This will pull out the code for the module fc_res = NCA_fetch_code(state)  # This will use patterns defined for the site to detect # columns. In this example we are detecting the id column: id_col = NCA_find_col(   patterns = state[[\"MC\"]][[\"detect_col\"]][[\"id\"]],   dscols   = names(ds$DS)) #> Warning: Unknown or uninitialised column: `DS`.  # This creates a new analysis state = NCA_new_ana(state)"},{"path":"/reference/NCA_test_mksession.html","id":null,"dir":"Reference","previous_headings":"","what":"Populate Session Data for Module Testing — NCA_test_mksession","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"Populates supplied session variable testing.","code":""},{"path":"/reference/NCA_test_mksession.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"","code":"NCA_test_mksession(   session,   id = \"NCA\",   id_UD = \"UD\",   id_DW = \"DW\",   id_ASM = \"ASM\",   full_session = TRUE )"},{"path":"/reference/NCA_test_mksession.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"session Shiny session variable (app) list (outside app) id ID string corresponds ID used call modules UI elements id_UD ID string corresponds ID used call UD modules UI elements id_DW ID string corresponds ID used call DW modules UI elements id_ASM ID string corresponds ID used call ASM modules UI elements full_session Boolean indicate full test session created (default TRUE).","code":""},{"path":"/reference/NCA_test_mksession.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"list following elements isgood: Boolean indicating exit status function. session: value Shiny session variable (app) list (outside app) initialization. input: value shiny input end session initialization. state: App state. rsc: react_state components.","code":""},{"path":"/reference/NCA_test_mksession.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populate Session Data for Module Testing — NCA_test_mksession","text":"","code":"sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:205e69e2051b7154e4a5792e4a97a35a"},{"path":"/reference/apply_route_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Route Mapping to Dataset — apply_route_map","title":"Applies Route Mapping to Dataset — apply_route_map","text":"Used convert nonstandard dose route values (.e. \"IV\") standard values (\"intravascular\").","code":""},{"path":"/reference/apply_route_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Route Mapping to Dataset — apply_route_map","text":"","code":"apply_route_map(route_map = list(), route_col = NULL, DS = NULL)"},{"path":"/reference/apply_route_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Route Mapping to Dataset — apply_route_map","text":"route_map List names corresponding route replacement vector regular expressions match. route_col Column name route data. DS Dataframe containing dataset.","code":""},{"path":"/reference/apply_route_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Route Mapping to Dataset — apply_route_map","text":"Dataset route mapping applied.","code":""},{"path":"/reference/apply_route_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies Route Mapping to Dataset — apply_route_map","text":"","code":"if(system.file(package=\"readxl\") !=\"\"){ library(readxl) #loading a dataset data_file =  system.file(package=\"formods\",\"test_data\",\"TEST_DATA.xlsx\") myDS = readxl::read_excel(path=data_file, sheet=\"DATA\")   route_map = list(    intravascular = c(\"^(?i)iv$\"),    extravascular = c(\"^(?i)sc$\", \"^(?i)oral\")  )  utils::head(myDS[[\"ROUTE\"]])  myDS = apply_route_map(route_map = route_map,                        route_col = \"ROUTE\",                        DS        = myDS)  utils::head(myDS[[\"ROUTE\"]]) } #> [1] \"intravascular\" \"intravascular\" \"intravascular\" \"intravascular\" #> [5] \"intravascular\" \"intravascular\""},{"path":"/reference/build_span.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct Table Span From Components — build_span","title":"Construct Table Span From Components — build_span","text":"Takes large table, common rows, header information constructs table subset components using supplied ranges rows columns.","code":""},{"path":"/reference/build_span.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct Table Span From Components — build_span","text":"","code":"build_span(   table_body = NULL,   row_common = NULL,   table_body_head = NULL,   row_common_head = NULL,   header_format = \"text\",   obnd = NULL,   row_sel = NULL,   col_sel = NULL,   table_alignment = \"center\",   inner_border = officer::fp_border(color = \"black\", width = 0.3),   outer_border = officer::fp_border(color = \"black\", width = 2),   set_header_inner_border_v = TRUE,   set_header_inner_border_h = TRUE,   set_header_outer_border = TRUE,   set_body_inner_border_v = TRUE,   set_body_inner_border_h = FALSE,   set_body_outer_border = TRUE,   notes_detect = NULL )"},{"path":"/reference/build_span.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct Table Span From Components — build_span","text":"table_body Data frame body large table. row_common Data frame common rows. table_body_head Data frame matrix headers table body. row_common_head Data frame matrix headers common rows. header_format Format header either \"text\" (default) \"md\" markdown. obnd Optional onbrand object used format markdown. default NULL value use default formatting. row_sel Indices rows build table . col_sel Indices columns build table . table_alignment Character string specifying alignment #'table (body headers). Can \"center\" (default), \"left\", \"right\", \"justify\" inner_border Border object inner border lines defined using officer::fp_border() outer_border Border object outer border lines defined using officer::fp_border() set_header_inner_border_v Boolean value enable disable inner vertical borders headers set_header_inner_border_h Boolean value enable disable inner horizontal borders headers set_header_outer_border Boolean value enable disable outer border headers set_body_inner_border_v Boolean value enable disable inner vertical borders body set_body_inner_border_h Boolean value enable disable inner horizontal borders body set_body_outer_border Boolean value enable disable outer border borders body notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")).","code":""},{"path":"/reference/build_span.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct Table Span From Components — build_span","text":"list following elements df:     Data frame built table. ft:     data frame flextable object. notes:  Note placeholders found table.","code":""},{"path":"/reference/build_span.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct Table Span From Components — build_span","text":"way data frames relate mapped . dimensions different data frames identified (nrow x ncol)","code":"#                            col_sel #                      |<--------------->| # #|--------------------------------------------| --- #|                 |   .                 .    |  ^ #|                 |   .                 .    |  | #| row_common_head |   . table_body_head .    |  | m #|      m x n      |   .      m x c      .    |  | #|                 |   .                 .    |  v #|--------------------------------------------| --- #|                 |   .                 .    |  ^ #|                 |   .                 .    |  | #|    row_common   |   .   table_body    .    |  | #|      r x n      |   .     r x c       .    |  | #|                 |   .                 .    |  | #|.................|..........................|  |     - #|                 |   ./  /  /  /  /  / .    |  |     ^ #|                 |   .  /  /  /  /  /  .    |  | r   | #|                 |   . /  /  /  /  /  /.    |  |     | row_sel #|                 |   ./  /  /  /  /  / .    |  |     | #|                 |   .  /  /  /  /  /  .    |  |     v #|.................|...../../../../../../.... |  |     - #|                 |   .                 .    |  | #|                 |   .                 .    |  v #|--------------------------------------------| --- # #|<--------------->|<------------------------>| #        n                    c"},{"path":"/reference/build_span.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct Table Span From Components — build_span","text":"","code":"library(formods) library(readxl) library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union library(tidyr)  # First we need to create some data.  # This will read in a test dataset: DS = readxl::read_excel(  path  = system.file(package=\"formods\",                       \"test_data\",                       \"TEST_DATA.xlsx\"),  sheet = \"DATA\")  # This will filter the dataset down and modify the formatting DS = dplyr::filter(DS, EVID == 0)                          |>   dplyr::filter(ID <= 30)                                  |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT  = ifelse(.data[[\"CMT\"]] == \"C_ng_ml\",                               \"Test Article\",                               .data[[\"CMT\"]]))              |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT = ifelse(.data[[\"CMT\"]] == \"BM_ng_ml\",                              \"Biomarker\",                              .data[[\"CMT\"]]))               |>   dplyr::rename(Analyte = \"CMT\")                           |>   dplyr::mutate(DV = ifelse(.data[[\"DV\"]] == 0, \"BQL\", .data[[\"DV\"]]))  # This represents the large table we want to split up into smaller tables  wide_df = tidyr::pivot_wider(DS,   values_from = \"DV\",   names_from  = \"ID\")               |>   dplyr::arrange(Analyte, TIME)     |>   dplyr::mutate(TIME = as.character(.data[[\"TIME\"]]))      # The first two columns represent the rows # that are common across the tables: row_common = wide_df[,1:2]  # The remaining columns represent the body of the table: table_body = wide_df[,3:ncol(wide_df)]  # Next we create matrices that contain the header  # information for each component above: row_common_head = matrix(   data  = c(\"Time\", \"Analyte\",             \"(hr)\", \"(ng/ml)\"),   ncol  = 2,   byrow = TRUE)  table_body_head = matrix(   data  = c(rep(\"Subject ID\", times=ncol(table_body)),              names(table_body)),   ncol  = ncol(table_body),   byrow = TRUE)   # This builds all of the tables: span_res = span_table(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       notes_detect    = c(\"BQL\"))  # This will show you the first one: span_res$tables[[\"Table 1\"]]$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`  #> header has 2 row(s)  #> body has 20 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4  # This will build a single table with the rows and  # column ranges specified bs_res =   build_span(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       col_sel         = c(1:10),                       row_sel         = c(1:10),                       notes_detect    = c(\"BQL\"))  # This will show you the result: bs_res$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`  #> header has 2 row(s)  #> body has 10 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6   7    8    9    10 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 643 1050 1130   744 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 644 1051 1134 745.9 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 645 1052 1138 747.8 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 647 1055 1146 751.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4 651 1059 1162 759.2"},{"path":"/reference/dose_records_builder.html","id":null,"dir":"Reference","previous_headings":"","what":"Builds Dose Records Dataframe — dose_records_builder","title":"Builds Dose Records Dataframe — dose_records_builder","text":"Takes information columns dataset constructs dosing records.","code":""},{"path":"/reference/dose_records_builder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Builds Dose Records Dataframe — dose_records_builder","text":"","code":"dose_records_builder(   NCA_DS = NULL,   dose_from = NULL,   col_id = NULL,   col_time = NULL,   col_ntime = NULL,   col_route = NULL,   col_dose = NULL,   col_cycle = NULL,   col_dur = NULL,   col_evid = NULL,   col_analyte = NULL,   col_group = NULL )"},{"path":"/reference/dose_records_builder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Builds Dose Records Dataframe — dose_records_builder","text":"NCA_DS Dataset containing dosing records. dose_from Method dose extraction either \"cols\" \"rows\". col_id Name column subject ID. col_time Name column time since first dose. col_ntime Name column time since last dose (required dose_from=\"cols\"). col_route Name column route information. col_dose Name column last dose given. col_cycle Name column dose cycle (required dose_from=\"cols\"). col_dur Name column dose duration. col_evid Name column event ID (required dose_from=\"rows\"). col_analyte Name column analyte (optional). col_group Names columns grouping information (optionl).","code":""},{"path":"/reference/dose_records_builder.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Builds Dose Records Dataframe — dose_records_builder","text":"list containing following elements isgood:           Return status function. msgs:             Messages passed back user. dose_rec:","code":""},{"path":"/reference/dose_records_builder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Builds Dose Records Dataframe — dose_records_builder","text":"","code":"if(system.file(package=\"readxl\") !=\"\"){ library(dplyr) library(readxl) library(stringr)  # Example data file: data_file =  system.file(package=\"formods\",\"test_data\",\"TEST_DATA.xlsx\")  # Dataset formatted to extract dosing from columns DS_cols = readxl::read_excel(path=data_file, sheet=\"DATA\")        |>  dplyr::filter(EVID == 0)                                |>  dplyr::filter(DOSE %in% c(3))                           |>  dplyr::filter(str_detect(string=Cohort, \"^MD\"))         |>  dplyr::filter(CMT == \"C_ng_ml\")  drb_res = dose_records_builder(  NCA_DS     = DS_cols,  dose_from  = \"cols\",  col_id     = \"ID\",  col_time   = \"TIME_DY\",  col_ntime  = \"NTIME_DY\",  col_route  = \"ROUTE\",  col_cycle  = \"DOSE_NUM\",  col_dose   = \"DOSE\",  col_group  = \"Cohort\")  utils::head(drb_res$dose_rec)  # Dataset formatted to extract dosing from rows (records) DS_rows = readxl::read_excel(path=data_file, sheet=\"DATA\")        |>  dplyr::filter(DOSE %in% c(3))                                   |>  dplyr::filter(str_detect(string=Cohort, \"^MD\"))                 |>  dplyr::filter(CMT %in% c(\"Ac\", \"C_ng_ml\"))  drb_res = dose_records_builder(  NCA_DS     = DS_rows,  dose_from  = \"rows\",  col_id     = \"ID\",  col_time   = \"TIME_DY\",  col_ntime  = \"NTIME_DY\",  col_route  = \"ROUTE\",  col_dose   = \"AMT\",  col_evid   = \"EVID\",  col_group  = \"Cohort\")  utils::head(drb_res$dose_rec) } #> Warning: package ‘dplyr’ was built under R version 4.2.3 #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> Warning: package ‘stringr’ was built under R version 4.2.3 #> # A tibble: 0 × 5 #> # ℹ 5 variables: ID <dbl>, TIME_DY <dbl>, AMT <dbl>, ROUTE <chr>, Cohort <chr>"},{"path":"/reference/fetch_rxinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Information from an rxode2 Object — fetch_rxinfo","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"provide information like parameter names, covriates, etc rxode2 object.","code":""},{"path":"/reference/fetch_rxinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"","code":"fetch_rxinfo(object)"},{"path":"/reference/fetch_rxinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"object rxode2 model object  ID string corresponds ID used call modules UI elements","code":""},{"path":"/reference/fetch_rxinfo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"List following elements. elements: List names simulation elements: covariates: Names covariates system. parameters: Names parameters (subject level) system. iiv: Names iiv parameters system. states: Names states/compartments system.","code":""},{"path":"/reference/fetch_rxinfo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Information from an rxode2 Object — fetch_rxinfo","text":"","code":"library(formods) library(ggplot2) #> Warning: package ‘ggplot2’ was built under R version 4.2.3  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #> Warning: package ‘rxode2’ was built under R version 4.2.3 #> rxode2 2.1.2 using 1 threads (see ?getRxThreads) #>   no cache: create with `rxCreateCache()` #> ======================================== #> rxode2 has not detected OpenMP support and will run in single-threaded mode #> This is a Mac. Please read https://mac.r-project.org/openmp/ #> ======================================== #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/fetch_rxtc.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts Timecourse and Merges Covariates — fetch_rxtc","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"Takes output rxSolve() merges missing covariates present params sim","code":""},{"path":"/reference/fetch_rxtc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"","code":"fetch_rxtc(rx_details, sim)"},{"path":"/reference/fetch_rxtc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"rx_details Output fetch_rxinfo() sim output rxSolve()","code":""},{"path":"/reference/fetch_rxtc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracts Timecourse and Merges Covariates — fetch_rxtc","text":"Dataframe simulated time course.","code":""},{"path":"/reference/ft_apply_md.html","id":null,"dir":"Reference","previous_headings":"","what":"Render Markdown in flextable Object — ft_apply_md","title":"Render Markdown in flextable Object — ft_apply_md","text":"Takes flextable object renders markdown specified part.","code":""},{"path":"/reference/ft_apply_md.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Render Markdown in flextable Object — ft_apply_md","text":"","code":"ft_apply_md(ft, obnd = NULL, part = \"body\")"},{"path":"/reference/ft_apply_md.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Render Markdown in flextable Object — ft_apply_md","text":"ft Flextable object. obnd Optional onbrand object used format markdown. default NULL value use default formatting. part Part table can one \"\", \"body\" (default), \"header\", \"footer\"","code":""},{"path":"/reference/ft_apply_md.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Render Markdown in flextable Object — ft_apply_md","text":"flextable markdown applied","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"Takes output PKNCA creates ggplot figures faceted subject id highlighting certain NCA aspects (e.g. points used half-life)","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"","code":"mk_figure_ind_obs(   nca_res,   OBS_LAB = \"Concentration ===CONCUNITS===\",   TIME_LAB = \"Time ===TIMEUNITS===\",   OBS_STRING = \"Observation\",   BLQ_STRING = \"BLQ\",   NA_STRING = \"Missing\",   log_scale = TRUE,   scales = \"fixed\",   nfrows = 4,   nfcols = 3 )"},{"path":"/reference/mk_figure_ind_obs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"nca_res Output PKNCA. OBS_LAB Label observation axis optional ===CONCUNITS=== placeholder units. TIME_LAB Label time axis optional ===TIMEUNITS=== placeholder units. OBS_STRING Label observation data. BLQ_STRING Label BLQ data. NA_STRING Label missing data. log_scale Boolean variable control y-scale (TRUE: Log 10, FALSE: linear). scales String determine scales used faceting. Can either \"fixed\", \"free\", \"free_x\", \"free_y\". nfrows Number facet rows per page. nfcols Number facet cols per page.","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"List containing element figures list figure pages (\"Figure 1\", \"Figure 2\", etc.). also list containing two elements: gg:    ggplot object page. notes: Placeholder future notes, NULL now.","code":""},{"path":"/reference/mk_figure_ind_obs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates Figures of Individual Observations from PKNCA Result — mk_figure_ind_obs","text":"","code":"id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a state variable to be define sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:aa766d3936aae9435fc7d2cb3db4cdf7  state = sess_res$state  # Pulls out the active analysis current_ana = NCA_fetch_current_ana(state)  # This is the raw PKNCA output pknca_res = NCA_fetch_ana_pknca(state, current_ana)  # Building the figure mk_res = mk_figure_ind_obs(nca_res = pknca_res) mk_res$figures$Figure_1$gg"},{"path":"/reference/mk_rx_obj.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes an rxode2 Object — mk_rx_obj","title":"Makes an rxode2 Object — mk_rx_obj","text":"Creates rxode2 object model (either rxode2 function NONMEM file)","code":""},{"path":"/reference/mk_rx_obj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes an rxode2 Object — mk_rx_obj","text":"","code":"mk_rx_obj(type, model)"},{"path":"/reference/mk_rx_obj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes an rxode2 Object — mk_rx_obj","text":"type Type supplied model can \"rxode2\", \"NONMEM\" model List containing relevant information model. depend model types. rxode2: supplied model rxode2 format. fcn_def: Character string containing function definition. fcn_obj: Name funciton object created fcn_def. NONMEM: supplied model NONMEM format (either control model_file: Character string containing NONMEM model file.","code":""},{"path":"/reference/mk_rx_obj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes an rxode2 Object — mk_rx_obj","text":"Results FM_tc() running model. include field isgood boolean variable indicating success failure. See documentation FM_tc() format returned evaluation results failure address . successful capture field contain following: fcn_obj: function name. rx_obj: built rxode2 object.","code":""},{"path":"/reference/mk_rx_obj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Makes an rxode2 Object — mk_rx_obj","text":"","code":"fcn_def = ' my_func = function ()    {        description <- \"One compartment PK model with linear clearance\"        ini({            lka <- 0.45            label(\"Absorption rate (Ka)\")            lcl <- 1            label(\"Clearance (CL)\")            lvc <- 3.45            label(\"Central volume of distribution (V)\")            propSd <- c(0, 0.5)            label(\"Proportional residual error (fraction)\")        })        model({            ka <- exp(lka)            cl <- exp(lcl)            vc <- exp(lvc)            cp <- linCmt()            cp ~ prop(propSd)        })     }' fcn_obj = \"my_func\" model = list(fcn_def = fcn_def,              fcn_obj = fcn_obj)   rx_res = mk_rx_obj(\"rxode2\", model) #>   #>   #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments  # function object rx_res[[\"capture\"]][[\"fcn_obj\"]] #> function ()  #> { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         cp <- linCmt() #>         cp ~ prop(propSd) #>     }) #> } #> <environment: 0x140b10510>  # rxode2 object rx_res[[\"capture\"]][[\"rx_obj\"]] #>  ── rxode2-based solved PK 1-compartment model with first-order absorption ──────  #>  ── Initalization: ──   #> Fixed Effects ($theta):  #>    lka    lcl    lvc propSd  #>   0.45   1.00   3.45   0.50  #>  ── Model (Normalized Syntax): ──  #> function() { #>     description <- \"One compartment PK model with linear clearance\" #>     ini({ #>         lka <- 0.45 #>         label(\"Absorption rate (Ka)\") #>         lcl <- 1 #>         label(\"Clearance (CL)\") #>         lvc <- 3.45 #>         label(\"Central volume of distribution (V)\") #>         propSd <- c(0, 0.5) #>         label(\"Proportional residual error (fraction)\") #>     }) #>     model({ #>         ka <- exp(lka) #>         cl <- exp(lcl) #>         vc <- exp(lvc) #>         cp <- linCmt() #>         cp ~ prop(propSd) #>     }) #> }"},{"path":"/reference/mk_subjects.html","id":null,"dir":"Reference","previous_headings":"","what":"Fetches Information from an rxode2 Object — mk_subjects","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"provide information like parameter names, covriates, etc rxode2 object.","code":""},{"path":"/reference/mk_subjects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"","code":"mk_subjects(object, nsub = 10, covs = NULL)"},{"path":"/reference/mk_subjects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"object rxode2 model object  ID string corresponds ID used call modules UI elements. nsub Number subjects generate. covs List describing covariates generated.","code":""},{"path":"/reference/mk_subjects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"List following elements. isgood:     Return status function. msgs:       Error warning messages issues encountered. subjects:   Data frame parameters covariates subjects generated. iCov:       Data frame covariates. params:     Data frame parameters.","code":""},{"path":"/reference/mk_subjects.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"See . underlying simulations run using rxode2, need rxode2 system object. can either simulate subjects load file. Next need define set rules. set conditions actions. evaluation time point conditions evaluated. condition met actions associated condition executed. example, visit (evaluation time point) trough PK certain level (condition) may want increase dosing regimen next dosing cycle (action).","code":""},{"path":"/reference/mk_subjects.html","id":"creating-subjects","dir":"Reference","previous_headings":"","what":"Creating subjects","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/reference/mk_subjects.html","id":"covariates","dir":"Reference","previous_headings":"","what":"Covariates","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. values: field depends type optional sampling . fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/reference/mk_subjects.html","id":"rule-based-simulations","dir":"Reference","previous_headings":"","what":"Rule-based simulations","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/reference/mk_subjects.html","id":"rules","dir":"Reference","previous_headings":"","what":"Rules","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). action: rule trigger can following: type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: action type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: action type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: action type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/reference/mk_subjects.html","id":"rule-evaluation-environment","dir":"Reference","previous_headings":"","what":"Rule-evaluation environment","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/reference/mk_subjects.html","id":"the-following-functions-will-be-available-","dir":"Reference","previous_headings":"","what":"The following functions will be available:","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":[]},{"path":"/reference/mk_subjects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fetches Information from an rxode2 Object — mk_subjects","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/mk_table_ind_obs.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"Takes output PKNCA creates tabular view individual observation data. can spread several tables (pages) necessary.","code":""},{"path":"/reference/mk_table_ind_obs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"","code":"mk_table_ind_obs(   nca_res,   obnd = NULL,   not_sampled = \"NS\",   blq = \"BLQ\",   digits = 3,   text_format = \"text\",   max_height = 7,   max_width = 6.5,   max_row = NULL,   max_col = 9,   notes_detect = NULL,   rows_by = \"time\" )"},{"path":"/reference/mk_table_ind_obs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"nca_res Output PKNCA. obnd onbrand reporting object. not_sampled Character string use missing data pivoting. blq Character string use indicate data level quantification (value 0 dataset). digits Number significant figures report (set NULL disable rounding) text_format Either \"md\" markdown \"text\" (default) plain text. max_height Maximum height final table inches (value NULL use 100 inches). max_width Maximum width final table inches (value NULL use 100 inches). max_row Maximum number rows page. Spillover hang side page.. max_col Maximum number columns page. Spillover wrapped multiple pages. notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")). rows_by Can either \"time\" \"id\". \"time\", column time separate column subject ID. rows_by set \"id\" column ID column individual time.","code":""},{"path":"/reference/mk_table_ind_obs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"List containing following elements isgood:      Boolean indicating exit status function. one_table:   Dataframe entire table first lines containing header. one_body:    Dataframe entire table (data ). one_header:  Dataframe entire header (row body, data). tables:      Named list tables. list element output msgs:        Vector text messages describing errors found. format build_span.","code":""},{"path":"/reference/mk_table_ind_obs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates Tables of Individual Observations from PKNCA Result — mk_table_ind_obs","text":"Time Concentration (ng/mL) ID (day) 1 2 3 0 BLQ BLQ BLQ 1 690 750 670 2 588 577 576 3 506 508 501 4 438 476 441 7 303 425 320 21 123 274 139 28 95.5 220 104 35 75.8 177 77.8 42 60.6 142 58.6","code":"id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a state variable to be define sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:f1d2ff38b4c9fc2abdcc109d17292a24  state = sess_res$state  # Pulls out the active analysis current_ana = NCA_fetch_current_ana(state)  # This is the raw PKNCA output pknca_res = NCA_fetch_ana_pknca(state, current_ana)  # Building the figure mk_res = mk_table_ind_obs(nca_res = pknca_res) mk_res$tables[[\"Table 1\"]]$ft .cl-9c77c794{}.cl-9c74b2ac{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-9c75d86c{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-9c75e0c8{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0d2{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0d3{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0d4{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0dc{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0e6{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0e7{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0e8{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0f0{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0f1{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0f2{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9c75e0fa{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}TimeConcentration (ng/mL) by ID(day)1230BLQBLQBLQ16907506702588577576350650850144384764417303425320211232741392895.52201043575.817777.84260.614258.6"},{"path":"/reference/mk_table_nca_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Tabular Output from PKNCA Results — mk_table_nca_params","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"Create paginated tables PKNCA use reports Shiny apps.","code":""},{"path":"/reference/mk_table_nca_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"","code":"mk_table_nca_params(   nca_res,   type = \"individual\",   grouping = \"interval\",   not_calc = \"NC\",   obnd = NULL,   nps = NULL,   mult_str = \"*\",   infinity = \"inf\",   digits = NULL,   text_format = \"text\",   notes_detect = NULL,   max_height = 7,   max_width = 6.5,   max_row = NULL,   max_col = NULL )"},{"path":"/reference/mk_table_nca_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"nca_res Output PKNCA. type Type table generate. Can either \"individual\" \"summary\"]. grouping group columns tables. Can either \"interval\" \"parameter\"]. not_calc Text string replace NA values indicated values calculated. obnd onbrand reporting object. nps NCA parameter summary table following columns. parameter:      PKNCA Paramter name. text:           Name used text output. md:             Name used markdown output. latex:          Name used latex output. description:    Verbose textual description parameter. mult_str Text string replace * values units. infinity Text string replace infinity time intervals column headers. digits Number significant figures report (set NULL disable rounding) text_format Either \"md\" markdown \"text\" (default) plain text. notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")). max_height Maximum height final table inches (value NULL use 100 inches). max_width Maximum width final table inches (value NULL use 100 inches). max_row Maximum number rows page. Spillover hang side page.. max_col Maximum number columns page. Spillover wrapped multiple pages.","code":""},{"path":"/reference/mk_table_nca_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"list containing following elements raw_nca:     Raw PKNCA output. isgood:      Boolean indicating exit status function. one_table:   Dataframe entire table first lines containing header. one_body:    Dataframe entire table (data ). one_header:  Dataframe entire header (row body, data). tables:      Named list tables. list element output msgs:        Vector text messages describing errors found. format build_span.","code":""},{"path":"/reference/mk_table_nca_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Tabular Output from PKNCA Results — mk_table_nca_params","text":"ID AUClast Cmax AUCinf,obs  day*ng/mL ng/mL day*ng/mL [0,21] [0,inf] 1 5,900 690 9,590 2 8,240 750 17,000 3 6,140 670 9,520","code":"id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a state variable to be define sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:951d3cc4bcfe04cbdd51235b5d3cb130  state = sess_res$state  # Pulls out the active analysis current_ana = NCA_fetch_current_ana(state)  # This is the raw PKNCA output pknca_res = NCA_fetch_ana_pknca(state, current_ana)  # Parameter reporting details from the ruminate configuration nps  = state[[\"NCA\"]][[\"nca_parameters\"]][[\"summary\"]]  # Building the figure mk_res = mk_table_nca_params(nca_res = pknca_res, nps=nps, digits=3) mk_res$tables[[\"Table 1\"]]$ft .cl-9d50e100{}.cl-9d4dfa8a{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1474{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 2pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f147e{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f147f{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1480{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0.3pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1488{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1492{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0.3pt solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f1493{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f149c{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f149d{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f14a6{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 2pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f14a7{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 0.3pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-9d4f14a8{width:0.75in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.3pt solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}IDAUClastCmaxAUCinf,obsday*ng/mLng/mLday*ng/mL[0,21][0,inf]15,9006909,59028,24075017,00036,1406709,520"},{"path":"/reference/nca_builder.html","id":null,"dir":"Reference","previous_headings":"","what":"Builds NCA Code from ui Elements — nca_builder","title":"Builds NCA Code from ui Elements — nca_builder","text":"Takes current analysis state object creates code run analysis","code":""},{"path":"/reference/nca_builder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Builds NCA Code from ui Elements — nca_builder","text":"","code":"nca_builder(state)"},{"path":"/reference/nca_builder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Builds NCA Code from ui Elements — nca_builder","text":"state NCA state NCA_fetch_state() JMH update return list ","code":""},{"path":"/reference/nca_builder.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Builds NCA Code from ui Elements — nca_builder","text":"NCA state NCA current analysis built.","code":""},{"path":"/reference/nca_builder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Builds NCA Code from ui Elements — nca_builder","text":"","code":"# Module IDs id     = \"NCA\" id_UD  = \"UD\" id_DW  = \"DW\" id_ASM = \"ASM\"  # We need a module variables to be defined sess_res = NCA_test_mksession(session=list(),      id     = id,      id_UD  = id_UD,      id_DW  = id_DW,      id_ASM = id_ASM,      full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:9a77ea921271e7e3fa800559c31869f6  state = sess_res$state  state = nca_builder(state) #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion"},{"path":"/reference/plot_sr_ev.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots        Timecourse of Rules Simulations — plot_sr_ev","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"","code":"Plots the timecourse of `simulate_rules()` output."},{"path":"/reference/plot_sr_ev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"","code":"plot_sr_ev(   sro = NULL,   fpage = 1,   fcol = \"id\",   error_msgs = NULL,   ylog = TRUE,   ylab_str = \"Amount\",   xlab_str = \"Time\",   post_proc = \"fig  = fig + ggplot2::theme_light()\",   evplot = c(1, 4),   fncol = 4,   fnrow = 2 )"},{"path":"/reference/plot_sr_ev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"sro Output 'simulate_rules()'. fpage facets selected multiple pages generated indcates       page return. fcol Name column facet NULL disable faceting (\"id\"). error_msgs Named list error messages overwrite (NULL ylog Boolean enable log10 scaling y-axis (TRUE ylab_str Label y-axis (\"Output\" xlab_str Label x-axis (\"Output\" post_proc Character object post processing post-processing code figure object named fig internall  (\"fig = fig + theme_light()\") evplot Evids plot can 1 4 fncol Number columns faceted output. fnrow Number rows faceted output.","code":""},{"path":"/reference/plot_sr_ev.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"List followin1g elements: isgood:       Return status function. msgs:         Error warning messages issues encountered. npages:       Total number pages using current configuration. error_msgs:   List error messages used. dsp:          Intermediate dataset generated sro plot ggplot. fig:          Figure generated.","code":""},{"path":"/reference/plot_sr_ev.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"detailed examples see vignette(\"clinical_trial_simulation\", package = \"ruminate\").","code":""},{"path":"/reference/plot_sr_ev.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots        Timecourse of Rules Simulations — plot_sr_ev","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/plot_sr_tc.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots        Timecourse of Rules Simulations — plot_sr_tc","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"","code":"Plots the timecourse of `simulate_rules()` output."},{"path":"/reference/plot_sr_tc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"","code":"plot_sr_tc(   sro = NULL,   dvcols = NULL,   fpage = 1,   fcol = \"id\",   error_msgs = NULL,   ylog = TRUE,   ylab_str = \"Output\",   xlab_str = \"Time\",   post_proc = \"fig  = fig + ggplot2::theme_light()\",   fncol = 4,   fnrow = 2 )"},{"path":"/reference/plot_sr_tc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"sro Output 'simulate_rules()'. dvcols Character vector dependent variables. fpage facets selected multiple pages generated indcates       page return. fcol Name column facet NULL disable faceting (\"id\"). error_msgs Named list error messages overwrite (NULL ylog Boolean enable log10 scaling y-axis (TRUE ylab_str Label y-axis (\"Output\" xlab_str Label x-axis (\"Output\" post_proc Character object post processing post-processing code figure object named fig internall  (\"fig = fig + theme_light()\") fncol Number columns faceted output. fnrow Number rows faceted output.","code":""},{"path":"/reference/plot_sr_tc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"List followin1g elements: isgood:       Return status function. msgs:         Error warning messages issues encountered. npages:       Total number pages using current configuration. error_msgs:   List error messages used. dsp:          Intermediate dataset generated sro plot ggplot. fig:          Figure generated.","code":""},{"path":"/reference/plot_sr_tc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"detailed examples see vignette(\"clinical_trial_simulation\", package = \"ruminate\").","code":""},{"path":"/reference/plot_sr_tc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots        Timecourse of Rules Simulations — plot_sr_tc","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/ruminate.html","id":null,"dir":"Reference","previous_headings":"","what":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"done creating Shiny interface different tools data transformation (dplyr tidyr), plotting (ggplot2), noncompartmental analysis (PKNCA). results can reported Excel, Word PowerPoint. state app can saved loaded later date. saved, script generated reproduce different actions Shiny interface. Runs pharmacometrics ruminate app.","code":""},{"path":"/reference/ruminate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"","code":"ruminate(   host = \"127.0.0.1\",   port = 3838,   server_opts = list(shiny.maxRequestSize = 30 * 1024^2),   mksession = FALSE )"},{"path":"/reference/ruminate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"host Hostname server (\"127.0.0.1\") port Port number app (3838) server_opts List options (names) vlues (value) e.g. list(shiny.maxRequestSize = 30 * 1024^2). mksession Boolean value, TRUE load test session data app testing","code":""},{"path":"/reference/ruminate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"Nothing returned, function just runs built-ruminate app.","code":""},{"path":[]},{"path":"/reference/ruminate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"Maintainer: John Harrold john.m.harrold@gmail.com (ORCID)","code":""},{"path":"/reference/ruminate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ruminate: Shiny app and module to facilitate pharamacometrics analysis — ruminate","text":"","code":"if (interactive()) { ruminate() }"},{"path":"/reference/ruminate_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks ruminate Dependencies — ruminate_check","title":"Checks ruminate Dependencies — ruminate_check","text":"Looks suggested dependencies checks make sure installed.","code":""},{"path":"/reference/ruminate_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks ruminate Dependencies — ruminate_check","text":"","code":"ruminate_check(verbose = TRUE)"},{"path":"/reference/ruminate_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks ruminate Dependencies — ruminate_check","text":"verbose Logical indicating messages displayed","code":""},{"path":"/reference/ruminate_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks ruminate Dependencies — ruminate_check","text":"List following elements: all_found:    Boolean indicating packages found found_pkgs:   Character vector found packages missing_pkgs: Character vector missing packages","code":""},{"path":"/reference/ruminate_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks ruminate Dependencies — ruminate_check","text":"","code":"fcres =ruminate_check() #>  #> ── Checking ruminate for suggested packages ──────────────────────────────────── #> ✔ found clipr #> ✔ found gridExtra #> ✔ found knitr #> ✔ found nlmixr2lib #> ✔ found nonmem2rx #> ✔ found prompter #> ✔ found rmarkdown #> ✔ found readxl #> ✔ found rxode2et #> ✔ found shinydashboard #> ✔ found testthat #> ✔ found ubiquity"},{"path":"/reference/run_nca_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs NCA for the Current Analysis — run_nca_components","title":"Runs NCA for the Current Analysis — run_nca_components","text":"Takes current state runs current analysis state.","code":""},{"path":"/reference/run_nca_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs NCA for the Current Analysis — run_nca_components","text":"","code":"run_nca_components(   state,   components = c(\"nca\", \"fg_ind_obs\", \"tb_ind_obs\", \"tb_ind_params\") )"},{"path":"/reference/run_nca_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs NCA for the Current Analysis — run_nca_components","text":"state NCA state NCA_fetch_state() components List components run. default run following. just need regenerate figure based current nca results can just specify component. valid components: nca:               Run NCA analysis fg_ind_obs:        Build figure(s) indiviudal observations. tb_ind_obs:        Build table(s) indiviudal observations. tb_ind_params:     Build table(s) indiviudal parameters.","code":""},{"path":"/reference/run_nca_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs NCA for the Current Analysis — run_nca_components","text":"List following components: isgood:    Return status function. msgs:      Error messages issues encountered. nca_res:   PKNCA results run successful.","code":""},{"path":"/reference/run_nca_components.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runs NCA for the Current Analysis — run_nca_components","text":"","code":"# We need a state object to use below sess_res = NCA_test_mksession(session=list(), full_session=FALSE) #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → UD:   dest:   file.path(\"config\",\"report.docx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → UD:   dest:   file.path(\"config\",\"report.pptx\") #> → UD: including file #> → UD:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → UD:   dest:   file.path(\"config\",\"report.yaml\") #> → UD: State initialized #> → UD: module checksum updated:897d952fecbc804999396a96f9df4b20 #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → DW:   dest:   file.path(\"config\",\"report.docx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → DW:   dest:   file.path(\"config\",\"report.pptx\") #> → DW: including file #> → DW:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → DW:   dest:   file.path(\"config\",\"report.yaml\") #> → DW: State initialized #> → DW: module checksum updated:5b0f0b05ee3ac7336a74c564bb6efdad #> → DW: module checksum updated:65801ab4b3c645aabf7edf91fc2f0d2c #> → DW: module checksum updated:7fb2dd3b16b1780c4c24f64d658dd7c7 #> → DW: module checksum updated:5ab04ea00be44c0040695a4ed6243c90 #> → DW: module checksum updated:960db107cfe9db93578f79f0ae6283e2 #> → DW: module checksum updated:f0b5d122dcb04f207cba233253b61c4b #> → DW: module checksum updated:189ed77a84bd9fcdd7b26abff8bea34a #> → DW: module checksum updated:669ec77d50652669cd3fcfd949469b37 #> → DW: module checksum updated:dd6cafa0be3b6b195d230a11e9f07f9f #> → DW: module checksum updated:5c2022c376ca8f56b839347156559890 #> → DW: module checksum updated:43640500a04674debd8e2399056c384a #> → DW: module checksum updated:f81c45ac1ef4e7b43153d8c8eaf7aed6 #> → DW: module checksum updated:207cde891d98e20c4afbb7fb5fb0aba2 #> → DW: module checksum updated:0a673e81c01cbbf33533dd65d6baf9e3 #> → DW: module checksum updated:af3f3aefd0115ed9dfdd4d6ef9d5182f #> → DW: module checksum updated:b0ccaee21e859322df0bf79d00c4b347 #> → DW: module checksum updated:2d3d6e9e38be6dea813b2d4d679e34b5 #> → DW: module checksum updated:2b60dd49b48a4e65200dc7241ad270e3 #> → DW: module checksum updated:3c92f397181a4fcdbdf2bd7dd7425e77 #> → DW: module checksum updated:92b81d7573def603649370b87c123b55 #> → DW: module checksum updated:9210264aabc0339e832b0c6bcfda32af #> → DW: module checksum updated:fe62fde1e7e738a1a35c479de9d5007b #> → DW: module checksum updated:ff3edcd4c1e58c96ea2707361916ffbc #> → DW: module checksum updated:b6cdfcef0432c5d9b1fa2bd46122ffa2 #> → DW: module checksum updated:4fa4283f1fd8fd6cf6e6e7df0669723a #> → DW: module checksum updated:cf3e139cef554b2d09dca619ee6aff41 #> → DW: module checksum updated:a9154d91a9e4b5d21f9585a6f68dd083 #> → DW: module checksum updated:4b5c6ad66658ddfa92d10ae1b0b7afa8 #> → DW: module checksum updated:591ac875a490b66a1fa7f4e7d4ba1c62 #> → DW: module checksum updated:afa3ca309cc3e6a48f542f4e8eadd54a #> → DW: module checksum updated:5c918dd4f53a4f1e56115abb09c4f332 #> → DW: module checksum updated:159ddf8c8cd26b178dfda5434bbecfa1 #> → DW: module checksum updated:8bb3c97e700998f2f73353189bd07831 #> → DW: module checksum updated:a1eceffec3ff7d9f9961683b3d07088b #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.docx\") #> → NCA:   dest:   file.path(\"config\",\"report.docx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.pptx\") #> → NCA:   dest:   file.path(\"config\",\"report.pptx\") #> → NCA: including file #> → NCA:   source: file.path(system.file(package=\"onbrand\"), \"templates\", \"report.yaml\") #> → NCA:   dest:   file.path(\"config\",\"report.yaml\") #> → NCA: State initialized #> → NCA: State initialized #> → NCA: loading analysis scenario: sd_iv #> → NCA: NCA_add_int: append #> → NCA: NCA_add_int: append #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:53d39c9297f09f7aea6cbfe0c1583966 state = sess_res$state  state = run_nca_components(state, components=\"tb_ind_params\") #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> → NCA: module checksum updated:53d39c9297f09f7aea6cbfe0c1583966"},{"path":"/reference/simulate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Rule-Based simulates — simulate_rules","title":"Rule-Based simulates — simulate_rules","text":"Simulate rxode2 model based rules evaluated specified time-points. example want titrate dosing based individual plasma levels create rule changes dosing specified time points based last observation user.","code":""},{"path":"/reference/simulate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rule-Based simulates — simulate_rules","text":"","code":"simulate_rules(   object,   subjects,   eval_times,   output_times,   rules,   rx_options = list(),   preamble = \"\",   smooth_sampling = TRUE )"},{"path":"/reference/simulate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rule-Based simulates — simulate_rules","text":"object rxode2 model object  ID string corresponds ID used call modules UI elements subjects Dataframe subject level information. eval_times Vector simulation times evaulate rules (units time units system). output_times Specific output times include. times included well ensure proper evaluation rules. rules List rules, see description format. rx_options List options pass rxSolve(). preamble Character string user-defined code execute rule-evaluation environment (e.g. can put user-defined functions ). smooth_sampling Boolean TRUE insert sampling just dosing make sampling smooth.","code":""},{"path":"/reference/simulate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rule-Based simulates — simulate_rules","text":"List following elements: isgood:     Return status function. msgs:       Error warning messages issues encountered. simall:     Simulation results. ev_history: event table entire simulation history. eval_times: Evaluation time points","code":""},{"path":"/reference/simulate_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rule-Based simulates — simulate_rules","text":"detailed examples see vignette(\"clinical_trial_simulation\", package = \"ruminate\") underlying simulations run using rxode2, need rxode2 system object. can either simulate subjects load file. Next need define set rules. set conditions actions. evaluation time point conditions evaluated. condition met actions associated condition executed. example, visit (evaluation time point) trough PK certain level (condition) may want increase dosing regimen next dosing cycle (action).","code":""},{"path":"/reference/simulate_rules.html","id":"creating-subjects","dir":"Reference","previous_headings":"","what":"Creating subjects","title":"Rule-Based simulates — simulate_rules","text":"Subjects expected data frame following column headers: id Individual subject id Names parameters iiv specified ini section rxode2 function specification Names covariates used model. mk_subjects() — Creates subjects simulation sampling based -subject variability generating covariate information based user specifications.","code":""},{"path":"/reference/simulate_rules.html","id":"covariates","dir":"Reference","previous_headings":"","what":"Covariates","title":"Rule-Based simulates — simulate_rules","text":"covs input list following structure: type: Can either “fixed”, “discrete”, “continuous”. sampling: field needed “continuous” covariate ’ type can either “random”, “normal” “log-normal”. values: field depends type optional sampling . fixed: single value. discrete: vector possible discrete elements. continuous, random: Two values first lower bound second upper bound. continuous, normal: Two values first mean second variance. continuous, log-normal: Two values first mean second variance. examples shows SEX_ID randomly sampled values specified, SUBTYPE_ID fixed value, WT sampled log-normal distribution.","code":"covs = list(   SEX_ID     = list(type     = \"discrete\",                      values   = c(0,1)),   SUBTYPE_ID = list(type     = \"fixed\",                     values   = c(0)),   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )"},{"path":"/reference/simulate_rules.html","id":"rule-based-simulations","dir":"Reference","previous_headings":"","what":"Rule-based simulations","title":"Rule-Based simulates — simulate_rules","text":"simulate_rules() — run simulations based rule definitions .","code":""},{"path":"/reference/simulate_rules.html","id":"rules","dir":"Reference","previous_headings":"","what":"Rules","title":"Rule-Based simulates — simulate_rules","text":"Rules named list list name can short descriptive label used remember rule . names returned columns simulated data frame. condition: Character string evaluates either TRUE FALSE. true action portion triggered. list objects available see Rule-evaluation environment . fail_flag: Flag set rule_id column condition met (set \"false\" specified). true_flag: Flag set rule_id column condition met (set \"true\" specified). action: rule trigger can following: type: defines action type can either \"dose\", \"set state\", \"manual\". Based type action field expect different elements. Dosing: action type: \"dose\" values: Character string evaluates numeric vector dosing amounts (e.g. \"c(3,  3,  3,  3)\") times: Character string evaluates numeric vector times (e.g. \"c(0, 14, 28, 42)\") durations: Character string evaluates numeric vector durations (e.g. \"c(0, 0, 0, 0)\", zero bolus dosing) Changing state value: action type: \"set state\" state: Character string name state set (\"Ac\") value: Character string evaluates numeric value state (e.g. \"Ac/2\" set state half value Ac evaluation point) Manual modification simulation: action type: \"manual\" code: Character string code evaluate.","code":""},{"path":"/reference/simulate_rules.html","id":"rule-evaluation-environment","dir":"Reference","previous_headings":"","what":"Rule-evaluation environment","title":"Rule-Based simulates — simulate_rules","text":"Beyond simple simulations necessary execute actions based current previous state system. reason, condition elements action (e.g., values, times durations dose action type) evaluated, following objects available evaluation point: states: value named state compartment. covariates: value named covariate. subject-level parameters: value named parameter. rule value: last value rule evaluated . SI_SUB_HISTORY: data frame simulation history current subject current evaluation point. SI_subjects: subjects data frame. SI_eval_times: Vector evaluation times. SI_interval_ev: events table ’s current state given simulation interval. SI_ev_history: history event table containing events leading current interval. SI_ud_history: free form object user can define within “manual”action type.","code":""},{"path":"/reference/simulate_rules.html","id":"the-following-functions-will-be-available-","dir":"Reference","previous_headings":"","what":"The following functions will be available:","title":"Rule-Based simulates — simulate_rules","text":"SI_fpd: function fetch previous dose (fpd) given id state. example current id state Ac following:","code":"SI_fpd(id=id, state=\"Ac\")"},{"path":"/reference/simulate_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rule-Based simulates — simulate_rules","text":"","code":"library(formods) library(ggplot2)  # For more information see the Clinical Trial Simulation vignette: # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html  # None of this will work if rxode2 isn't installed: if(is_installed(\"rxode2\")){ library(rxode2) set.seed(8675309) rxSetSeed(8675309)  my_model = function ()  {     description <- \"One compartment PK model with linear clearance using differential equations\"     ini({         lka <- 0.45         label(\"Absorption rate (Ka)\")         lcl <- 1         label(\"Clearance (CL)\")         lvc <- 3.45         label(\"Central volume of distribution (V)\")         propSd <- c(0, 0.5)         label(\"Proportional residual error (fraction)\")         etalcl ~ 0.1     })     model({         ka <- exp(lka)         cl <- exp(lcl + etalcl)         vc <- exp(lvc)         kel <- cl/vc         d/dt(depot) <- -ka * depot         d/dt(central) <- ka * depot - kel * central         Cc <- central/vc         Cc ~ prop(propSd)     }) }  # This creates an rxode2 object object  = rxode(my_model)  # If you want details about the parameters, states, etc # in the model you can use this: rxdetails = fetch_rxinfo(object)  rxdetails$elements  # Next we will create subjects. To do that we need to  # specify information about covariates: nsub = 2 covs = list(   WT         = list(type     = \"continuous\",                     sampling = \"log-normal\",                     values   = c(70, .15)) )  subs = mk_subjects(object = object,                    nsub   = nsub,                    covs   = covs)  head(subs$subjects)  rules = list(   dose = list(     condition = \"TRUE\",     action    = list(       type  = \"dose\",       state     = \"central\",        values    = \"c(1)\",       times     = \"c(0)\",       durations = \"c(0)\")     ) )  # We evaulate the rules for dosing at time 0 eval_times =  0  # Stop 2 months after the last dose output_times = seq(0, 56, 1)  # This runs the rule-based simulations simres =    simulate_rules(     object        = object,     subjects      = subs[[\"subjects\"]],     eval_times    = eval_times,     output_times  = output_times,      rules         = rules)  # First subject data: sub_1 = simres$simall[simres$simall$id == 1, ]  # First subjects events evall = as.data.frame(simres$evall) ev_sub_1 = evall[evall$id ==1, ]  # All of the simulation data simall = simres$simall simall$id = as.factor(simall$id)  # Timecourse psim =    plot_sr_tc(     sro    = simres,     dvcols = \"Cc\") psim$fig  # Events pev =    plot_sr_ev(     sro    = simres,     ylog   = FALSE) pev$fig  } #>   #>   #>   #>   #>   #>   #> Warning: multi-subject simulation without without 'omega' #>   #>   #> Warning: multi-subject simulation without without 'omega' #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic? #> `geom_line()`: Each group consists of only one observation. #> ℹ Do you need to adjust the group aesthetic?"},{"path":"/reference/span_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Spread Large Table Over Smaller Tables — span_table","title":"Spread Large Table Over Smaller Tables — span_table","text":"Takes large table spreads smaller tables paginate . preserve common row  information left separate columns according maximum specifications. final tables widths less equal max_col max_width, heights less equal max_row max_height.","code":""},{"path":"/reference/span_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spread Large Table Over Smaller Tables — span_table","text":"","code":"span_table(   table_body = NULL,   row_common = NULL,   table_body_head = NULL,   row_common_head = NULL,   header_format = \"text\",   obnd = NULL,   max_row = 20,   max_col = 10,   max_height = 7,   max_width = 6.5,   table_alignment = \"center\",   inner_border = officer::fp_border(color = \"black\", width = 0.3),   outer_border = officer::fp_border(color = \"black\", width = 2),   set_header_inner_border_v = TRUE,   set_header_inner_border_h = TRUE,   set_header_outer_border = TRUE,   set_body_inner_border_v = TRUE,   set_body_inner_border_h = FALSE,   set_body_outer_border = TRUE,   notes_detect = NULL )"},{"path":"/reference/span_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spread Large Table Over Smaller Tables — span_table","text":"table_body Data frame body large table. row_common Data frame common rows. table_body_head Data frame matrix headers table body. row_common_head Data frame matrix headers common rows. header_format Format header either \"text\" (default) \"md\" markdown. obnd Optional onbrand object used format markdown. default NULL value use default formatting. max_row Maximum number rows output tables (value NULL set max_row number rows table). max_col Maximum number columns output tables (value NULL set max_col number columns table). max_height Maximum height final table inches (value NULL use 100 inches). max_width Maximum width final table inches (value NULL use 100 inches). table_alignment Character string specifying alignment #'table (body headers). Can \"center\" (default), \"left\", \"right\", \"justify\" inner_border Border object inner border lines defined using officer::fp_border() outer_border Border object outer border lines defined using officer::fp_border() set_header_inner_border_v Boolean value enable disable inner vertical borders headers set_header_inner_border_h Boolean value enable disable inner horizontal borders headers set_header_outer_border Boolean value enable disable outer border headers set_body_inner_border_v Boolean value enable disable inner vertical borders body set_body_inner_border_h Boolean value enable disable inner horizontal borders body set_body_outer_border Boolean value enable disable outer border borders body notes_detect Vector strings detect output tables (example c(\"NC\", \"BLQ\")).","code":""},{"path":"/reference/span_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spread Large Table Over Smaller Tables — span_table","text":"list following elements isgood:    Boolean indicating exit status function. one_body:  Full table headers. one_table: Full table headers. msgs:      Vector text messages describing errors found. tables:    Named list tables. list element output. format build_span().","code":""},{"path":"/reference/span_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spread Large Table Over Smaller Tables — span_table","text":"way data frames relate mapped . dimensions different data frames identified (nrow x ncol)","code":"#|-------------------------------------| --- #|                 |                   |  ^ #|                 |                   |  | #| row_common_head |  table_body_head  |  | m #|      m x n      |       m x c       |  | #|                 |                   |  v #|-------------------------------------| --- #|                 |                   |  ^ #|                 |                   |  | #|    row_common   |    table_body     |  | r #|      r x n      |      r x c        |  | #|                 |                   |  | #|                 |                   |  v #|-------------------------------------| --- # #|<--------------->|<----------------->| #        n                   c"},{"path":[]},{"path":"/reference/span_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spread Large Table Over Smaller Tables — span_table","text":"","code":"library(formods) library(readxl) library(dplyr) library(tidyr)  # First we need to create some data.  # This will read in a test dataset: DS = readxl::read_excel(  path  = system.file(package=\"formods\",                       \"test_data\",                       \"TEST_DATA.xlsx\"),  sheet = \"DATA\")  # This will filter the dataset down and modify the formatting DS = dplyr::filter(DS, EVID == 0)                          |>   dplyr::filter(ID <= 30)                                  |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT  = ifelse(.data[[\"CMT\"]] == \"C_ng_ml\",                               \"Test Article\",                               .data[[\"CMT\"]]))              |>   dplyr::select(ID, TIME, DV, CMT)                         |>   dplyr::mutate(CMT = ifelse(.data[[\"CMT\"]] == \"BM_ng_ml\",                              \"Biomarker\",                              .data[[\"CMT\"]]))               |>   dplyr::rename(Analyte = \"CMT\")                           |>   dplyr::mutate(DV = ifelse(.data[[\"DV\"]] == 0, \"BQL\", .data[[\"DV\"]]))  # This represents the large table we want to split up into smaller tables  wide_df = tidyr::pivot_wider(DS,   values_from = \"DV\",   names_from  = \"ID\")               |>   dplyr::arrange(Analyte, TIME)     |>   dplyr::mutate(TIME = as.character(.data[[\"TIME\"]]))      # The first two columns represent the rows # that are common across the tables: row_common = wide_df[,1:2]  # The remaining columns represent the body of the table: table_body = wide_df[,3:ncol(wide_df)]  # Next we create matrices that contain the header  # information for each component above: row_common_head = matrix(   data  = c(\"Time\", \"Analyte\",             \"(hr)\", \"(ng/ml)\"),   ncol  = 2,   byrow = TRUE)  table_body_head = matrix(   data  = c(rep(\"Subject ID\", times=ncol(table_body)),              names(table_body)),   ncol  = ncol(table_body),   byrow = TRUE)   # This builds all of the tables: span_res = span_table(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       notes_detect    = c(\"BQL\"))  # This will show you the first one: span_res$tables[[\"Table 1\"]]$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`  #> header has 2 row(s)  #> body has 20 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4  # This will build a single table with the rows and  # column ranges specified bs_res =   build_span(table_body      = table_body,                       row_common      = row_common,                       table_body_head = table_body_head,                       row_common_head = row_common_head,                       col_sel         = c(1:10),                       row_sel         = c(1:10),                       notes_detect    = c(\"BQL\"))  # This will show you the result: bs_res$ft #> a flextable object. #> col_keys: `TIME`, `Analyte`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`  #> header has 2 row(s)  #> body has 10 row(s)  #> original dataset sample:  #>   TIME   Analyte    1    2     3    4    5     6   7    8    9    10 #> 1    0 Biomarker 1250 1030   831 1170 1140   868 643 1050 1130   744 #> 2 0.25 Biomarker 1255 1032 832.2 1172 1145 869.4 644 1051 1134 745.9 #> 3  0.5 Biomarker 1259 1034 833.4 1174 1149 870.9 645 1052 1138 747.8 #> 4    1 Biomarker 1269 1037 835.8 1178 1158 873.7 647 1055 1146 751.7 #> 5    2 Biomarker 1287 1045 840.5 1186 1176 879.4 651 1059 1162 759.2"},{"path":"/news/index.html","id":"ruminate-022-development-version","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.2 (development version)","title":"ruminate 0.2.2 (development version)","text":"CRAN release: 2024-03-14 Added CTS (Clinical trial simulator) module (development)","code":""},{"path":"/news/index.html","id":"ruminate-022","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.2","title":"ruminate 0.2.2","text":"CRAN release: 2024-03-14 Fixed error default ruminate app causing crash Separated ruminate.R sample app two separate (default one one developent.)","code":""},{"path":"/news/index.html","id":"ruminate-021","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.1","title":"ruminate 0.2.1","text":"CRAN release: 2023-12-11 Fixed use suggests conditional.","code":""},{"path":"/news/index.html","id":"ruminate-020","dir":"Changelog","previous_headings":"","what":"ruminate 0.2.0","title":"ruminate 0.2.0","text":"CRAN release: 2023-12-02 Support rxode2 NONMEM models Fixed bug NCA module column details didn’t update properly switching analyses. Fixed bug detecting dosing rows left dosing records data frame subsequent NCA. Fixed bug parameter(s) added interval resulting [[numeric]] reported values. Now time interval added subsequent additions update parameters interval. Fixed stand-alone code generation copy clipboard.","code":""},{"path":"/news/index.html","id":"ruminate-011","dir":"Changelog","previous_headings":"","what":"ruminate 0.1.1","title":"ruminate 0.1.1","text":"CRAN release: 2023-08-29 Initial release","code":""}]
diff --git a/inst/test_apps/CTS_funcs.R b/inst/test_apps/CTS_funcs.R
index 68df27f..5224119 100644
--- a/inst/test_apps/CTS_funcs.R
+++ b/inst/test_apps/CTS_funcs.R
@@ -2,6 +2,7 @@
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_add_covariate.Rd b/man/CTS_add_covariate.Rd
index 0e5b823..9bc0432 100644
--- a/man/CTS_add_covariate.Rd
+++ b/man/CTS_add_covariate.Rd
@@ -26,9 +26,9 @@ Takes the ui elements in the module state and processes the covariate elements f
 \details{
 This depends on the following UI values in the state
 \itemize{
-\item{} state[\link{"CTS"}][\link{"ui"}][\link{"covariate_value"}]
-\item{} state[\link{"CTS"}][\link{"ui"}][\link{"covariate_type_selected"}]
-\item{} state[\link{"CTS"}][\link{"ui"}][\link{"selected_covariate"}]
+\item{} \code{state[["CTS"]][["ui"]][["covariate_value"]]}
+\item{} \code{state[["CTS"]][["ui"]][["covariate_type_selected"]]}
+\item{} \code{state[["CTS"]][["ui"]][["selected_covariate"]]}
 }
 }
 \examples{
@@ -36,6 +36,7 @@ This depends on the following UI values in the state
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_add_rule.Rd b/man/CTS_add_rule.Rd
index 320db9c..1236bc0 100644
--- a/man/CTS_add_rule.Rd
+++ b/man/CTS_add_rule.Rd
@@ -64,6 +64,7 @@ This depends on the following UI values in the state and element
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_change_source_model.Rd b/man/CTS_change_source_model.Rd
index d80cb28..a84685f 100644
--- a/man/CTS_change_source_model.Rd
+++ b/man/CTS_change_source_model.Rd
@@ -28,6 +28,7 @@ This depends on the following UI values in the state.
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_del_current_element.Rd b/man/CTS_del_current_element.Rd
index 2e26ba7..3d4dcf7 100644
--- a/man/CTS_del_current_element.Rd
+++ b/man/CTS_del_current_element.Rd
@@ -21,6 +21,7 @@ If that is the last element, then a new default will be added.
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_fetch_code.Rd b/man/CTS_fetch_code.Rd
index 9c92fe5..361f774 100644
--- a/man/CTS_fetch_code.Rd
+++ b/man/CTS_fetch_code.Rd
@@ -20,6 +20,7 @@ Fetches the code to generate results seen in the app
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_fetch_current_element.Rd b/man/CTS_fetch_current_element.Rd
index 3435efe..ae047f3 100644
--- a/man/CTS_fetch_current_element.Rd
+++ b/man/CTS_fetch_current_element.Rd
@@ -23,6 +23,7 @@ cohort
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_new_element.Rd b/man/CTS_new_element.Rd
index bc448f6..e1976ad 100644
--- a/man/CTS_new_element.Rd
+++ b/man/CTS_new_element.Rd
@@ -23,6 +23,7 @@ and makes this new cohort the active cohort.
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_plot_element.Rd b/man/CTS_plot_element.Rd
index 49ad6c4..739dfcb 100644
--- a/man/CTS_plot_element.Rd
+++ b/man/CTS_plot_element.Rd
@@ -28,6 +28,7 @@ rules.
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_set_current_element.Rd b/man/CTS_set_current_element.Rd
index 7ab6f0e..25526f7 100644
--- a/man/CTS_set_current_element.Rd
+++ b/man/CTS_set_current_element.Rd
@@ -24,6 +24,7 @@ cohort
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_simulate_element.Rd b/man/CTS_simulate_element.Rd
index a37cdae..ac127f3 100644
--- a/man/CTS_simulate_element.Rd
+++ b/man/CTS_simulate_element.Rd
@@ -24,6 +24,7 @@ rules.
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)
diff --git a/man/CTS_update_checksum.Rd b/man/CTS_update_checksum.Rd
index cff2001..6e8141b 100644
--- a/man/CTS_update_checksum.Rd
+++ b/man/CTS_update_checksum.Rd
@@ -21,6 +21,7 @@ downstream updates
 # https://ruminate.ubiquity.tools/articles/clinical_trial_simulation.html
 
 # None of this will work if rxode2 isn't installed:
+library(formods)
 if(is_installed("rxode2")){
 
 sess_res = CTS_test_mksession(session=list(), full_session=FALSE)