Issue 72: Rename model (#97)

* Rename Stan folder to latent_individual

* Rename ltcad to latent_individual in R code

* Rename ltcad in vignette

* Update document

* Update pkgdown

NAMESPACE

@@ -2,15 +2,15 @@
 
 S3method(epidist,default)
 S3method(epidist_family,default)
-S3method(epidist_family,epidist_ltcad)
+S3method(epidist_family,epidist_latent_individual)
 S3method(epidist_formula,default)
-S3method(epidist_formula,epidist_ltcad)
+S3method(epidist_formula,epidist_latent_individual)
 S3method(epidist_prepare,default)
-S3method(epidist_prepare,epidist_ltcad)
+S3method(epidist_prepare,epidist_latent_individual)
 S3method(epidist_priors,default)
-S3method(epidist_priors,epidist_ltcad)
+S3method(epidist_priors,epidist_latent_individual)
 S3method(epidist_stancode,default)
-S3method(epidist_stancode,epidist_ltcad)
+S3method(epidist_stancode,epidist_latent_individual)
 export(add_natural_scale_mean_sd)
 export(calculate_censor_delay)
 export(calculate_cohort_mean)

R/defaults.R

@@ -1,15 +1,15 @@
 #' Default method used when preparing data
 #'
 #' @param model Character string, model type to prepare to use.
-#' Supported options are "ltcad".
+#' Supported options are "latent_individual".
 #' @param ... Additional arguments passed to model specific `epidist_prepare`
 #' functions
 #' @rdname epidist_prepare
 #' @method epidist_prepare default
 #' @family defaults
 #' @export
 epidist_prepare.default <- function(data, model, ...) {
-  model <- match.arg(model, choices = c("ltcad"))
+  model <- match.arg(model, choices = c("latent_individual"))
   class(data) <- c(class(data), paste0("epidist_", model))
   epidist_prepare(data, ...)
 }

R/ltcad.R → R/latent_individual.R

@@ -1,7 +1,7 @@
-#' @method epidist_prepare epidist_ltcad
-#' @family ltcad
+#' @method epidist_prepare epidist_latent_individual
+#' @family latent_individual
 #' @export
-epidist_prepare.epidist_ltcad <- function(data, ...) {
+epidist_prepare.epidist_latent_individual <- function(data, ...) {
   id <- obs_t <- obs_at <- ptime_lwr <- pwindow_upr <- stime_lwr <- NULL
   stime_upr <- woverlap <- swindow_upr <- stime_upr <- delay_central <- NULL
   row_id <- ptime_upr <- NULL
@@ -25,24 +25,24 @@ epidist_prepare.epidist_ltcad <- function(data, ...) {
   return(data)
 }
 
-#' @method epidist_priors epidist_ltcad
-#' @family ltcad
+#' @method epidist_priors epidist_latent_individual
+#' @family latent_individual
 #' @export
-epidist_priors.epidist_ltcad <- function(data, ...) {
+epidist_priors.epidist_latent_individual <- function(data, ...) {
   return(NULL)
 }
 
-#' Define a formula for the ltcad model
+#' Define a formula for the latent_individual model
 #'
 #' @param data ...
 #' @param delay_central Formula for the delay mean. Defaults to intercept only.
 #' @param sigma Formula for the delay standard deviation. Defaults to intercept
 #' only.
 #' @param ... ...
-#' @method epidist_formula epidist_ltcad
-#' @family ltcad
+#' @method epidist_formula epidist_latent_individual
+#' @family latent_individual
 #' @export
-epidist_formula.epidist_ltcad <- function(data, delay_central = ~ 1,
+epidist_formula.epidist_latent_individual <- function(data, delay_central = ~ 1,
                                           sigma = ~ 1, ...) {
   delay_equation <- paste0(
     "delay_central | vreal(obs_t, pwindow_upr, swindow_upr)",
@@ -54,10 +54,10 @@ epidist_formula.epidist_ltcad <- function(data, delay_central = ~ 1,
   return(form)
 }
 
-#' @method epidist_family epidist_ltcad
-#' @family ltcad
+#' @method epidist_family epidist_latent_individual
+#' @family latent_individual
 #' @export
-epidist_family.epidist_ltcad <- function(data, family = "lognormal", ...) {
+epidist_family.epidist_latent_individual <- function(data, family = "lognormal", ...) {
   brms::custom_family(
     paste0("latent_", family),
     dpars = c("mu", "sigma"),
@@ -70,15 +70,15 @@ epidist_family.epidist_ltcad <- function(data, family = "lognormal", ...) {
   )
 }
 
-#' @method epidist_stancode epidist_ltcad
-#' @family ltcad
+#' @method epidist_stancode epidist_latent_individual
+#' @family latent_individual
 #' @export
-epidist_stancode.epidist_ltcad <- function(data,
+epidist_stancode.epidist_latent_individual <- function(data,
                                            family = epidist_family(data), ...) {
   stanvars_version <- epidist_version_stanvar()
 
   stanvars_functions <- brms::stanvar(
-    block = "functions", scode = epidist_stan_chunk("ltcad/functions.stan")
+    block = "functions", scode = epidist_stan_chunk("latent_individual/functions.stan")
   )
 
   family_name <- gsub("latent_", "", family$name)
@@ -107,15 +107,15 @@ epidist_stancode.epidist_ltcad <- function(data,
     )
 
   stanvars_parameters <- brms::stanvar(
-    block = "parameters", scode = epidist_stan_chunk("ltcad/parameters.stan")
+    block = "parameters", scode = epidist_stan_chunk("latent_individual/parameters.stan")
   )
 
   stanvars_tparameters <- brms::stanvar(
-    block = "tparameters", scode = epidist_stan_chunk("ltcad/tparameters.stan")
+    block = "tparameters", scode = epidist_stan_chunk("latent_individual/tparameters.stan")
   )
 
   stanvars_priors <- brms::stanvar(
-    block = "model", scode = epidist_stan_chunk("ltcad/priors.stan")
+    block = "model", scode = epidist_stan_chunk("latent_individual/priors.stan")
   )
 
   stanvars_all <- stanvars_version + stanvars_functions + stanvars_data +

_pkgdown.yml

@@ -32,10 +32,10 @@ reference:
   desc: Default methods for S3 generics
   contents:
   - has_concept("defaults")
-- title: Latent truncation censoring adjusted delay model
-  desc: Model-specific methods
+- title: Latent individual model
+  desc: Specific methods for the latent individual model
   contents:
-  - has_concept("ltcad")
+  - has_concept("latent_individual")
 - title: Postprocess
   desc: Functions for postprocessing model output
   contents:

vignettes/epidist.Rmd

@@ -245,18 +245,16 @@ dplyr::bind_rows(
 ```
 
 The main function you will use for modelling is called `epidist`^[Technically, `epidist` is an [S3 generic](http://adv-r.had.co.nz/S3.html) which allows it to work differently for inputs of different classes. This is why inputs must be prepared first via `epidist_prepare` so that they are of the appropriate class!].
-We will fit the model `"ltcad"`, which stands for latent truncation censoring adjusted delay (LTCAD)^[Catchy!].
+We will fit the model `"latent_individual"` which uses latent variables for the time of primary and secondary event of each individual^[In a future vignette, we will explain in more detail the structure of the model!].
 To do so, we first prepare the `data` using `epidist_prepare`:
 
-<!-- In a future vignette, we will explain in more detail the structure of the model. -->
-
 ```{r}
-data <- epidist_prepare(obs_cens_trunc_samp, model = "ltcad")
+data <- epidist_prepare(obs_cens_trunc_samp, model = "latent_individual")
 class(data)
 ```
 
-The `data` object now has the class `epidist_ltcad`.
-Using this `data`, we now call `epidist::epidist` to fit the LTCAD model.
+The `data` object now has the class `epidist_latent_individual`.
+Using this `data`, we now call `epidist::epidist` to fit the model.
 The parameters of the model are inferred using Bayesian inference.
 In particular, we use the the No-U-Turn Sampler (NUTS) Markov chain Monte Carlo (MCMC) algorithm via the [`brms`](https://paul-buerkner.github.io/brms/) R package [@brms].