export stanfit getters

NAMESPACE

@@ -9,14 +9,41 @@ S3method(coef,dynamitefit)
 S3method(confint,dynamitefit)
 S3method(fitted,dynamitefit)
 S3method(formula,dynamitefit)
+S3method(get_algorithm,CmdStanMCMC)
+S3method(get_algorithm,CmdStanMCMC_CSV)
+S3method(get_algorithm,stanfit)
 S3method(get_code,dynamitefit)
 S3method(get_code,dynamiteformula)
 S3method(get_data,dynamitefit)
 S3method(get_data,dynamiteformula)
+S3method(get_diagnostics,CmdStanMCMC)
+S3method(get_diagnostics,CmdStanMCMC_CSV)
+S3method(get_diagnostics,stanfit)
+S3method(get_draws,CMdStanMCMC_CSV)
+S3method(get_draws,CmdStanMCMC)
+S3method(get_draws,stanfit)
+S3method(get_elapsed_time,CmdStanMCMC)
+S3method(get_elapsed_time,CmdStanMCMC_CSV)
+S3method(get_elapsed_time,stanfit)
+S3method(get_max_treedepth,CmdStanMCMC)
+S3method(get_max_treedepth,CmdStanMCMC_CSV)
+S3method(get_max_treedepth,stanfit)
+S3method(get_model_code,CmdStanMCMC)
+S3method(get_model_code,CmdStanMCMC_CSV)
+S3method(get_model_code,stanfit)
+S3method(get_nchains,CmdStanMCMC)
+S3method(get_nchains,CmdStanMCMC_CSV)
+S3method(get_nchains,stanfit)
+S3method(get_ndraws,CmdStanMCMC)
+S3method(get_ndraws,CmdStanMCMC_CSV)
+S3method(get_ndraws,stanfit)
 S3method(get_parameter_dims,dynamitefit)
 S3method(get_parameter_dims,dynamiteformula)
 S3method(get_parameter_names,dynamitefit)
 S3method(get_parameter_types,dynamitefit)
+S3method(get_pars_oi,CmdStanMCMC)
+S3method(get_pars_oi,CmdStanMCMC_CSV)
+S3method(get_pars_oi,stanfit)
 S3method(get_priors,dynamitefit)
 S3method(get_priors,dynamiteformula)
 S3method(hmc_diagnostics,dynamitefit)
@@ -41,11 +68,20 @@ export(aux)
 export(dynamice)
 export(dynamite)
 export(dynamiteformula)
+export(get_algorithm)
 export(get_code)
 export(get_data)
+export(get_diagnostics)
+export(get_draws)
+export(get_elapsed_time)
+export(get_max_treedepth)
+export(get_model_code)
+export(get_nchains)
+export(get_ndraws)
 export(get_parameter_dims)
 export(get_parameter_names)
 export(get_parameter_types)
+export(get_pars_oi)
 export(get_priors)
 export(hmc_diagnostics)
 export(lags)

R/as_data_table.R

@@ -561,7 +561,7 @@ as_data_table_omega <- function(x, draws, n_draws, response, category, ...) {
 #' @describeIn as_data_table_default Data Table for a "omega_alpha" Parameter
 #' @noRd
 as_data_table_omega_alpha <- function(x, draws, n_draws, response,
-                                     category, ...) {
+                                      category, ...) {
   D <- x$stan$model_vars$D
   data.table::data.table(
     parameter = rep(

R/dynamite-package.R

@@ -11,7 +11,7 @@
 #' * The package vignettes
 #' * [dynamite::dynamiteformula()] for information on defining models.
 #' * [dynamite::dynamite()] for information on fitting models.
-#' * \url{https://github.com/ropensci/dynamite/issues/} to submit a bug report
+#' * <https://github.com/ropensci/dynamite/issues/> to submit a bug report
 #'   or a feature request.
 #'
 #' # Authors
@@ -56,8 +56,8 @@
 #' coefficients vary according to a spline with 20 degrees of freedom.
 #'
 #' @family examples
-#' @source The data was generated according to a script in
-#' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/gaussian_example.R}
+#' @source The data was generated via `gaussian_example.R` in
+#' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 #' @format A data frame with 3000 rows and 5 variables:
 #' \describe{
 #'  \item{y}{The response variable.}
@@ -95,8 +95,8 @@
 #' }
 #' Note the very small number of samples due to size restrictions on CRAN.
 #' @family examples
-#' @source The data was generated according to a script in
-#' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/gaussian_example_fit.R}
+#' @source The data was generated via `gaussian_example_fit.R` in
+#' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 #' @format A `dynamitefit` object.
 "gaussian_example_fit"
 
@@ -120,8 +120,8 @@
 # #' )
 # #' }
 # #' @family examples
-# #' @source The data was generated according to a script in
-# #' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/gaussian_simulation_fit.R}
+# #' @source The data was generated via to `gaussian_simulation_fit.R` in
+# #' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 # #' @format A `dynamitefit` object.
 # "gaussian_simulation_fit"
 
@@ -131,8 +131,8 @@
 #' response variables of different distributions.
 #'
 #' @family examples
-#' @source The data was generated according to a script in
-#' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/multichannel_example.R}
+#' @source The data was generated via `multichannel_example.R` in
+#' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 #' @format A data frame with 3000 rows and 5 variables:
 #' \describe{
 #'  \item{id}{Variable defining individuals (1 to 50).}
@@ -171,8 +171,8 @@
 #' }
 #' Note the small number of samples due to size restrictions on CRAN.
 #' @family examples
-#' @source Script in
-#' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/multichannel_example_fit.R}
+#' @source THe data was generated via `multichannel_example_fit.R` in
+#' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 #' @format A `dynamitefit` object.
 "multichannel_example_fit"
 
@@ -182,8 +182,8 @@
 #' response variables.
 #'
 #' @family examples
-#' @source The data was generated according to a script in
-#' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/categorical_example.R}
+#' @source The data was generated via `categorical_example.R` in
+#' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 #' @format A data frame with 2000 rows and 5 variables:
 #' \describe{
 #'  \item{id}{Variable defining individuals (1 to 100).}
@@ -216,8 +216,8 @@
 #' }
 #' Note the small number of samples due to size restrictions on CRAN.
 #' @family examples
-#' @source Script in
-#' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/categorical_example_fit.R}
+#' @source The data was generated via `categorical_example_fit.R` in
+#' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 #' @format A `dynamitefit` object.
 "categorical_example_fit"
 
@@ -227,8 +227,8 @@
 # #' trajectories are defined by a latent factor and random intercept terms.
 # #'
 # #' @family examples
-# #' @source The data was generated according to a script in
-# #' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/latent_factor_example.R}
+# #' @source The data was generated via `latent_factor_example.R` in
+# #' <https://github.com/ropensci/dynamite/blob/main/data-raw/>
 # #' @format A data frame with 2000 rows and 3 variables:
 # #' \describe{
 # #'  \item{y}{A continuos variable.}
@@ -264,7 +264,7 @@
 # #' }
 # #' Note the very small number of samples due to size restrictions on CRAN.
 # #' @family examples
-# #' @source Script in
-# #' \url{https://github.com/ropensci/dynamite/blob/main/data-raw/latent_factor_example_fit.R}
+# #' @source The data was generated via `latent_factor_example_fit.R` in
+# #' <https://github.com/ropensci/dynamite/tree/main/data-raw/>
 # #' @format A `dynamitefit` object.
 # "latent_factor_example_fit"

R/dynamite.R

@@ -4,7 +4,7 @@
 #' Bayesian inference. The \pkg{dynamite} package supports a wide range of
 #' distributions and allows the user to flexibly customize the priors for the
 #' model parameters. The dynamite model is specified using standard \R formula
-#' syntax via [dynamite::dynamiteformula()]. For more information and examples,
+#' syntax via [dynamiteformula()]. For more information and examples,
 #' see 'Details' and the package vignettes.
 #'
 #' The best-case scalability of `dynamite` in terms of data size should be
@@ -17,7 +17,7 @@
 #' @family fitting
 #' @rdname dynamite
 #' @param dformula \[`dynamiteformula`]\cr The model formula.
-#'   See [dynamite::dynamiteformula()] and 'Details'.
+#'   See [dynamiteformula()] and 'Details'.
 #' @param data
 #'   \[`data.frame`, `tibble::tibble`, or `data.table::data.table`]\cr
 #'   The data that contains the variables in the model in long format.
@@ -38,22 +38,23 @@
 #'   group. In case of name conflicts with `data`, see the `group_var` element
 #'   of the return object to get the column name of the new variable.
 #' @param priors \[`data.frame`]\cr An optional data frame with prior
-#'   definitions. See [dynamite::get_priors()] and 'Details'.
+#'   definitions. See [get_priors()] and 'Details'.
 #' @param backend \[`character(1)`]\cr Defines the backend interface to Stan,
 #'   should be  either `"rstan"` (the default) or `"cmdstanr"`. Note that
 #'   `cmdstanr` needs to be installed separately as it is not on CRAN. It also
-#'   needs the actual `CmdStan` software. See https://mc-stan.org/cmdstanr/ for
-#'   details.
+#'   needs the actual `CmdStan` software. See <https://mc-stan.org/cmdstanr/>
+#'   for details.
 #' @param verbose \[`logical(1)`]\cr All warnings and messages are suppressed
 #'   if set to `FALSE`. Defaults to `TRUE`. Setting this to `FALSE` will also
 #'   disable checks for perfect collinearity in the model matrix.
 #' @param verbose_stan \[`logical(1)`]\cr This is the `verbose` argument for
 #'   [rstan::sampling()]. Defaults to `FALSE`.
 #' @param stanc_options \[`list()`]\cr This is the `stanc_options` argument
 #'   passed to the compile method of a `CmdStanModel` object via
-#'   [cmdstanr::cmdstan_model()] when `backend = "cmdstanr"`.
-#'   Defaults to `list("O0")`. To enable level one compiler optimizations,
-#'   use `list("O1")`.
+#'   `cmdstan_model()` when `backend = "cmdstanr"`. Defaults to `list("O0")`.
+#'   To enable level one compiler optimizations, use `list("O1")`.
+#'   See <https://mc-stan.org/cmdstanr/reference/cmdstan_model.html>
+#'   for details.
 #' @param threads_per_chain \[`integer(1)`]\cr A Positive integer defining the
 #'   number of parallel threads to use within each chain. Default is `1`. See
 #'   [rstan::rstan_options()] and [cmdstanr::sample()] for details.
@@ -76,11 +77,13 @@
 #'   combined with `model_code = TRUE`, which adds the Stan model code to the
 #'   return object.
 #' @param ... For `dynamite()`, additional arguments to [rstan::sampling()] or
-#'   [cmdstanr::sample()], such as `chains` and `cores` (`chains` and
-#'   `parallel_chains` in `cmdstanr`). For `summary()`, additional arguments to
-#'   [dynamite::as.data.frame.dynamitefit()]. For `print()`, further arguments
-#'   to the print method for tibbles (see [tibble::formatting]). Not used for
-#'   `formula()`.
+#'   the `$sample()` method of the `CmdStanModel` object
+#'   (see <https://mc-stan.org/cmdstanr/reference/model-method-sample.html>),
+#'   such as `chains` and `cores`
+#'   (`chains` and `parallel_chains` in `cmdstanr`). For `summary()`,
+#'   additional arguments to [as.data.frame.dynamitefit()]. For `print()`,
+#'   further arguments to the print method for tibbles
+#'   (see [tibble::formatting]). Not used for `formula()`.
 #' @return `dynamite` returns a `dynamitefit` object which is a list containing
 #'   the following components:
 #'
@@ -321,7 +324,8 @@ dynamite_check <- function(dformula, data, time, group, priors, verbose,
   )
   stopifnot_(
     checkmate::test_string(x = custom_stan_model, null.ok = TRUE),
-    "Argument {.arg custom_stan_model} must be a single {.cls character} string."
+    "Argument {.arg custom_stan_model}
+    must be a single {.cls character} string."
   )
   stopifnot_(
     !isTRUE(grepl("\\.stan$", custom_stan_model, perl = TRUE)) ||

R/families.R

@@ -18,7 +18,8 @@ dynamitefamily <- function(name, link) {
   )
   stopifnot_(
     is_supported_link(name, link),
-    "{.val {link}} is not a supported link function for a {.val {name}} channel."
+    "{.val {link}} is not a supported link function
+    for a {.val {name}} channel."
   )
   structure(
     list(name = name, link = link),

R/lags.R

@@ -363,7 +363,8 @@ parse_lags <- function(dformula, data, group_var, time_var, verbose) {
 #'   are stochastic.
 #' @param increment \[`logical()`]\cr  A vector indicating whether to add
 #'   the new lag term or not (e.g.,, whether it was already present or not).
-#' @param type \[`character(1)`]\cr Either `"fixed"`, `"varying"`, or `"random"`.
+#' @param type \[`character(1)`]\cr
+#'   Either `"fixed"`, `"varying"`, or `"random"`.
 #' @param lhs \[`character()`]\cr A vector of the new lagged variable names.
 #' @noRd
 parse_new_lags <- function(dformula, channels_stoch, increment, type, lhs) {
@@ -594,5 +595,3 @@ prepare_lagged_response <- function(dformula, lag_map,
   }
   y
 }
-
-

R/lfo.R

@@ -18,9 +18,9 @@
 #'   the LFO computations to the console.
 #' @param k_threshold \[`numeric(1)`]\cr Threshold for the Pareto k estimate
 #'   triggering refit. Default is 0.7.
-#' @param ... Additional arguments passed to [rstan::sampling()] or
-#'   [cmdstanr::sample()], such as `chains` and `cores` (`parallel_chains` in
-#'   `cmdstanr`).
+#' @param ... Additional arguments passed to [rstan::sampling()] or the
+#'   `$sample()` method of the `CmdStanModel` object, such as `chains` and
+#'   `cores` (`parallel_chains` in `cmdstanr`).
 #' @return An `lfo` object which is a `list` with the following components:
 #'
 #'   * `ELPD`\cr Expected log predictive density estimate.

R/prepare_stan_input.R

@@ -517,7 +517,7 @@ prepare_channel_default <- function(y, Y, channel, sampling,
       nzchar(category),
       priors[priors$response == y & priors$category == category, ],
       priors[priors$response == y, ]
-      )
+    )
     channel$prior_distr <- list()
     types <- priors$type
     loop_types <- intersect(

R/print.R

@@ -70,18 +70,18 @@ print.dynamitefit <- function(x, full_diagnostics = FALSE, ...) {
     if (mcmc_algorithm) {
       min_ess <- which.min(sumr$ess_bulk)
       cat("\nSmallest bulk-ESS: ", round(sumr$ess_bulk[min_ess]), " (",
-          sumr$variable[min_ess], ")",
-          sep = ""
+        sumr$variable[min_ess], ")",
+        sep = ""
       )
       min_ess <- which.min(sumr$ess_tail)
       cat("\nSmallest tail-ESS: ", round(sumr$ess_tail[min_ess]), " (",
-          sumr$variable[min_ess], ")",
-          sep = ""
+        sumr$variable[min_ess], ")",
+        sep = ""
       )
       max_rhat <- which.max(sumr$rhat)
       cat("\nLargest Rhat: ", round(sumr$rhat[max_rhat], 3), " (",
-          sumr$variable[max_rhat], ")",
-          sep = ""
+        sumr$variable[max_rhat], ")",
+        sep = ""
       )
       runtimes <- get_elapsed_time(x$stanfit)
       if (nrow(runtimes) > 2L) {