Add documentation for ppc_data and ppc_loo_pit_data

NEWS.md

@@ -1,5 +1,6 @@
 # bayesplot (development version)
 
+* Documentation added for `ppc_data()` and `ppc_loo_pit_data()` functions (#209)
 * New functions `mcmc_dots` and `mcmc_dots_by_chain` for dot plots of MCMC draws by @behramulukir (#402)
 * Default to `quantiles=100` for all dot plots by @behramulukir (#402)
 

R/ppc-distributions.R

@@ -22,6 +22,15 @@
 #'
 #' @section Plot Descriptions:
 #' \describe{
+#'   \item{`ppc_data()`}{
+#'    This function prepares data for plotting with **ggplot2**. It is the
+#'    data-preparation back end for all the `ppc_*()` plotting functions, and
+#'    users can call it directly to create custom PPC plots using ggplot2. The
+#'    function returns a data frame (or list of data frames) that can be used to
+#'    build ggplot objects. This is useful when you want to customize the
+#'    appearance of PPC plots beyond what the built-in plotting functions allow,
+#'    or when you want to create entirely new types of PPC visualizations.
+#'   }
 #'   \item{`ppc_hist(), ppc_freqpoly(), ppc_dens(), ppc_boxplot()`}{
 #'    A separate histogram, shaded frequency polygon, smoothed kernel density
 #'    estimate, or box and whiskers plot is displayed for `y` and each

R/ppc-loo.R

@@ -23,10 +23,21 @@
 #'   `ppc_loo_ribbon()`, `alpha` and `size`  are passed to
 #'   [ggplot2::geom_ribbon()].
 #'
-#' @template return-ggplot
+#' @template return-ggplot-or-data
 #'
 #' @section Plot Descriptions:
 #' \describe{
+#' \item{`ppc_loo_pit_data()`}{
+#'  This function prepares LOO-PIT data for plotting with **ggplot2**. It is
+#'  the data-preparation back end for the LOO-PIT plotting functions
+#'  (`ppc_loo_pit_overlay()`, `ppc_loo_pit_qq()`, and `ppc_loo_pit_ecdf()`),
+#'  and users can call it directly to create custom LOO-PIT plots using
+#'  ggplot2. The function computes the leave-one-out probability integral
+#'  transform (LOO-PIT) values and returns a data frame that can be used to
+#'  build ggplot objects. This is useful when you want to create custom
+#'  visualizations of LOO-PIT values beyond what the built-in plotting
+#'  functions provide.
+#' }
 #' \item{`ppc_loo_pit_overlay()`, `ppc_loo_pit_qq()`, `ppc_loo_pit_ecdf()`}{
 #'  The calibration of marginal predictions can be assessed using probability
 #'  integral transformation (PIT) checks. LOO improves the check by avoiding the