Merge branch 'master' into master

Author: jgabry

.github/workflows/test-coverage.yaml

@@ -18,7 +18,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/setup-r@v2
         with:

DESCRIPTION

@@ -26,7 +26,7 @@ Description: Plotting functions for posterior analysis, MCMC diagnostics,
 License: GPL (>= 3)
 URL: https://mc-stan.org/bayesplot/
 BugReports: https://github.com/stan-dev/bayesplot/issues/
-SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
+SystemRequirements: pandoc (>= 1.12.3)
 Depends:
     R (>= 4.1.0)
 Imports:

NEWS.md

@@ -1,7 +1,7 @@
 # bayesplot (development version)
 
-
 * New `show_marginal` argument to `ppd_*()` functions to show the PPD - the marginal predictive distribution by @mattansb (#425)
+* Use `rlang::warn()` and `rlang::inform()` for selected PPC user messages instead of base `warning()` and `message()`.
 * Standardize input validation errors in `ppc_km_overlay()` and interpolation helpers to use `rlang::abort()` for consistent error handling.
 * Fix assignment-in-call bug in `mcmc_rank_ecdf()` (#).
 * Replaced deprecated `dplyr` and `tidyselect` functions (`top_n`, `one_of`, `group_indices`) with their modern equivalents to ensure future compatibility. (#431)
@@ -11,6 +11,7 @@
 * Fixed test in `test-ppc-distributions.R` that incorrectly used `ppc_dens()` instead of `ppd_dens()` when testing PPD functions
 * 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)
+* Use `"neff_ratio"` consistently in diagnostic color scale helpers to avoid relying on partial matching of `"neff"`.
 
 # bayesplot 1.15.0
 

R/bayesplot-colors.R

@@ -189,7 +189,7 @@ color_scheme_view <- function(scheme = NULL) {
 
 #' @export
 print.bayesplot_scheme <- function(x, ...) {
-  tab <- data.frame(unlist(x, use.names = FALSE), stringsAsFactors = FALSE)
+  tab <- data.frame(unlist(x, use.names = FALSE))
   colnames(tab) <- attr(x, "scheme_name") %||% "hex_color"
   print(tab, ...)
 }

R/mcmc-diagnostics.R

@@ -255,8 +255,8 @@ mcmc_neff <- function(ratio, ..., size = NULL) {
       linetype = 2,
       linewidth = 0.25) +
     labs(y = NULL, x = expression(N[eff]/N)) +
-    scale_fill_diagnostic("neff") +
-    scale_color_diagnostic("neff") +
+    scale_fill_diagnostic("neff_ratio") +
+    scale_color_diagnostic("neff_ratio") +
     scale_x_continuous(
       breaks = breaks,
       # as.character truncates trailing zeroes, while ggplot default does not
@@ -287,8 +287,8 @@ mcmc_neff_hist <- function(ratio, ..., binwidth = NULL, bins = NULL, breaks = NU
       binwidth = binwidth,
       bins = bins,
       breaks = breaks) +
-    scale_color_diagnostic("neff") +
-    scale_fill_diagnostic("neff") +
+    scale_color_diagnostic("neff_ratio") +
+    scale_fill_diagnostic("neff_ratio") +
     labs(x = expression(N[eff]/N), y = NULL) +
     dont_expand_y_axis(c(0.005, 0)) +
     yaxis_title(FALSE) +
@@ -424,12 +424,12 @@ diagnostic_points <- function(size = NULL) {
 
 # Functions wrapping around scale_color_manual() and scale_fill_manual(), used to
 # color the intervals by rhat value
-scale_color_diagnostic <- function(diagnostic = c("rhat", "neff")) {
+scale_color_diagnostic <- function(diagnostic = c("rhat", "neff_ratio")) {
   d <- match.arg(diagnostic)
   diagnostic_color_scale(d, aesthetic = "color")
 }
 
-scale_fill_diagnostic <- function(diagnostic = c("rhat", "neff")) {
+scale_fill_diagnostic <- function(diagnostic = c("rhat", "neff_ratio")) {
   d <- match.arg(diagnostic)
   diagnostic_color_scale(d, aesthetic = "fill")
 }

R/mcmc-scatterplots.R

@@ -98,12 +98,12 @@
 #'    )
 #'
 #' # add ellipse
-#' p + stat_ellipse(level = 0.9, color = "gray20", size = 1)
+#' p + stat_ellipse(level = 0.9, color = "gray20", linewidth = 1)
 #'
 #' # add contour
 #' color_scheme_set("red")
 #' p2 <- mcmc_scatter(x, pars = c("alpha", "sigma"), size = 3.5, alpha = 0.25)
-#' p2 + stat_density_2d(color = "black", size = .5)
+#' p2 + stat_density_2d(color = "black", linewidth = .5)
 #'
 #' # can also add lines/smooths
 #' color_scheme_set("pink")

R/mcmc-traces.R

@@ -719,7 +719,7 @@ mcmc_trace_data <- function(x,
           color = guide_legend(order = 1),
           linetype = guide_legend(
             order = 2, title = NULL, keywidth = rel(1/2),
-            override.aes = list(size = rel(1/2)))
+            override.aes = list(linewidth = rel(1/2)))
         )
       }
     }

R/ppc-censoring.R

@@ -126,10 +126,10 @@ ppc_km_overlay <- function(
     abort("`extrapolation_factor` must be greater than or equal to 1.")
   }
   if (extrapolation_factor == 1.2) {
-    message(
+    inform(paste0(
       "Note: `extrapolation_factor` now defaults to 1.2 (20%).\n",
       "To display all posterior predictive draws, set `extrapolation_factor = Inf`."
-    )
+    ))
   }
 
   data <- ppc_data(y, yrep, group = status_y)

R/ppc-loo.R

@@ -202,15 +202,14 @@ ppc_loo_pit_overlay <- function(y,
     )
 
   if (!missing(y) && all(y %in% 0:1)) {
-    warning(
+    warn(paste0(
       "This plot is not recommended for binary data. ",
       "For plots that are more suitable see ",
-      "\nhttps://avehtari.github.io/modelselection/diabetes.html#44_calibration_of_predictions",
-      call. = FALSE
-    )
+      "\nhttps://avehtari.github.io/modelselection/diabetes.html#44_calibration_of_predictions"
+    ))
   }
 
-  message(paste(
+  inform(paste(
     "NOTE: The kernel density estimate assumes continuous observations",
     "and is not optimal for discrete observations."
   ))
@@ -797,7 +796,7 @@ ppc_loo_ribbon <-
   # 1-D Gaussian window filter. This method uses the "reflection method"
   # to estimate these pvalues and helps speed up the code
   if (any(is.infinite(x))) {
-    warning(paste(
+    warn(paste(
       "Ignored", sum(is.infinite(x)),
       "Non-finite PIT values are invalid for KDE boundary correction method"
     ))

R/ppc-test-statistics.R

@@ -449,9 +449,9 @@ Tyrep_label <- function() expression(italic(T)(italic(y)[rep]))
 
 message_if_using_mean <- function(stat) {
   if (is.character(stat) && stat == "mean") {
-    message(
+    inform(paste0(
       "Note: in most cases the default test statistic 'mean' is ",
       "too weak to detect anything of interest."
-    )
+    ))
   }
 }