deal with review comments

R/gen.R

@@ -3,15 +3,15 @@
 #'
 #' This function creates a function that draws from the posterior predictive
 #' distribution for a latent model using [primarycensored::rpcens()] to handle
-#' censoring and truncation. The returned function takes a prep argument from
+#' censoring and truncation. The returned function takes a `prep` argument from
 #' `brms` and returns posterior predictions. This is used internally by
 #' [brms::posterior_predict()] to generate predictions for latent models.
 #'
-#' @inheritParams epidist_family_model
+#' @inheritParams epidist_family
 #'
-#' @return A function that takes a prep argument from brms and returns a matrix
-#' of posterior predictions, with one row per posterior draw and one column
-#' per observation. The prep object must have the following variables:
+#' @return A function that takes a `prep` argument from brms and returns a
+#' matrix of posterior predictions, with one row per posterior draw and one
+#' column per observation. The `prep` object must have the following variables:
 #' * `vreal1`: relative observation time
 #' * `vreal2`: primary event window
 #' * `vreal3`: secondary event window
@@ -53,11 +53,11 @@ epidist_gen_posterior_predict <- function(family) {
 #'
 #' This function creates a function that calculates the expected value of the
 #' posterior predictive distribution for a latent model. The returned function
-#' takes a prep argument (from brms) and returns posterior expected values.
+#' takes a `prep` argument (from brms) and returns posterior expected values.
 #' This is used internally by [brms::posterior_epred()] to calculate expected
 #' values for latent models.
 #'
-#' @inheritParams epidist_family_model
+#' @inheritParams epidist_family
 #'
 #' @return A function that takes a prep argument from brms and returns a matrix
 #' of posterior expected values, with one row per posterior draw and one column

R/latent_model.R

@@ -109,7 +109,7 @@ epidist_family_model.epidist_latent_model <- function(
 #'
 #' @seealso [brms::log_lik()] for details on the brms log likelihood interface.
 #'
-#' @inheritParams epidist_family_model
+#' @inheritParams epidist_family
 #'
 #' @return A function that calculates the log likelihood for a single
 #' observation. The prep object must have the following variables:

R/utils.R

@@ -167,7 +167,7 @@
 #' @param prefix Character string prefix of the brms function to get (e.g.
 #' "log_lik")
 #'
-#' @param family A brms family object
+#' @inheritParams epidist_family
 #' @return The requested brms function
 #' @keywords internal
 .get_brms_fn <- function(prefix, family) {

vignettes/ebola.Rmd

@@ -315,7 +315,7 @@ ranef(fit_sex_district)
 
 To go further than summaries of the fitted model, we recommend using the `tidybayes` package.
 For example, to obtain the posterior expectation of the delay distribution, under no censoring or truncation, we may use the `modelr::data_grid()` function in combination with the `tidybayes::add_epred_draws()` function.
-The `tidybayes::add_epred_draws()` function uses the (internal) `posterior_epred_latent_lognormal()` function implemented in `epidist` for the `latent_lognormal` custom `brms` family.
+The `tidybayes::add_epred_draws()` function uses the `epidist_gen_posterior_predict()` function to generate a posterior prediction function for the `lognormal()` distribution.
 
 In Figure \@ref(fig:epred) we show the posterior expectation of the delay distribution for each of the three fitted models.
 Figure \@ref(fig:epred)B illustrates the higher mean of men as compared with women.

vignettes/epidist.Rmd

@@ -290,6 +290,9 @@ fit <- epidist(
 )
 ```
 
+**Note that here we use the default `rstan` backend but we generally recommend using the `cmdstanr` backend for faster sampling and additional features.**
+**This can be set using `backend = "cmdstanr"` after following the installing CmdStan instructions in the README.**
+
 The `fit` object is a `brmsfit` object containing MCMC samples from each of the parameters in the model, shown in the table below.
 Many of these parameters (e.g. `swindow` and `pwindow`) are the so called latent variables, and have lengths corresponding to the `sample_size`.
 
@@ -307,9 +310,6 @@ data.frame(
 Users familiar with Stan and `brms`, can work with `fit` directly.
 Any tool that supports `brms` fitted model objects will be compatible with `fit`.
 
-**Note that here we use the default `rstan` backend but we generally recommend using the `cmdstanr` backend for faster sampling and additional features.**
-**This can be set using `backend = "cmdstanr"` after following the installing CmdStan instructions in the README.**
-
 For example, we can use the built in `summary()` function
 
 ```{r}
@@ -341,7 +341,7 @@ ggplot() +
     data = data.frame(x = c(0, 30)),
     aes(x = x),
     fun = dlnorm,
-    linewidth = 2,
+    linewidth = 1.5,
     args = list(
       meanlog = secondary_dist[["mu"]],
       sdlog = secondary_dist[["sigma"]]