trying fixing R cmd check NOTE

R/helpers-mcmc.R

@@ -51,13 +51,13 @@ prepare_mcmc_array <- function(x,
   if (is.matrix(x)) {
     x <- x[, pars, drop=FALSE]
     if (length(transformations)) {
-      x <- apply_transformations(x, transformations)
+      x <- apply_transformations(x, transformations = transformations)
     }
     x <- array(x, dim = c(nrow(x), 1, ncol(x)))
   } else {
     x <- x[, , pars, drop = FALSE]
     if (length(transformations)) {
-      x <- apply_transformations(x, transformations)
+      x <- apply_transformations(x, transformations = transformations)
     }
   }
 
@@ -388,10 +388,10 @@ validate_transformations <-
 #'   functions.
 #' @return x, with tranformations having been applied to some parameters.
 #'
-apply_transformations <- function(x, transformations = list(), ...) {
+apply_transformations <- function(x, ...) {
   UseMethod("apply_transformations")
 }
-apply_transformations.matrix <- function(x, transformations = list()) {
+apply_transformations.matrix <- function(x, ..., transformations = list()) {
   pars <- colnames(x)
   x_transforms <- validate_transformations(transformations, pars)
   for (p in names(x_transforms)) {
@@ -400,7 +400,7 @@ apply_transformations.matrix <- function(x, transformations = list()) {
 
   x
 }
-apply_transformations.array <- function(x, transformations = list()) {
+apply_transformations.array <- function(x, ..., transformations = list()) {
   stopifnot(length(dim(x)) == 3)
   pars <- dimnames(x)[[3]]
   x_transforms <- validate_transformations(transformations, pars)

R/mcmc-diagnostics.R

@@ -360,17 +360,17 @@ mcmc_acf_bar <-
 #'   `x <= breaks[1]`, `breaks[1] < x <= breaks[2]`, `x > breaks[2]`).
 #' @return A factor the same length as `x` with three levels.
 #' @noRd
-diagnostic_factor <- function(x, breaks, ...) {
+diagnostic_factor <- function(x, ...) {
   UseMethod("diagnostic_factor")
 }
 
-diagnostic_factor.rhat <- function(x, breaks = c(1.05, 1.1)) {
+diagnostic_factor.rhat <- function(x, ..., breaks = c(1.05, 1.1)) {
   cut(x, breaks = c(-Inf, breaks, Inf),
       labels = c("low", "ok", "high"),
       ordered_result = FALSE)
 }
 
-diagnostic_factor.neff_ratio <- function(x, breaks = c(0.1, 0.5)) {
+diagnostic_factor.neff_ratio <- function(x, ..., breaks = c(0.1, 0.5)) {
   cut(x, breaks = c(-Inf, breaks, Inf),
       labels = c("low", "ok", "high"),
       ordered_result = FALSE)

tests/testthat/test-helpers-mcmc.R

@@ -193,11 +193,11 @@ test_that("validate_transformations throws correct errors", {
 test_that("apply_transformations works", {
   trans <- list('beta[1]' = "exp", sigma = function(x) x^2)
 
-  arr_trans <- apply_transformations(arr, trans)
+  arr_trans <- apply_transformations(arr, transformations = trans)
   expect_equal(arr_trans[,, "sigma"], arr[,, "sigma"]^2)
   expect_equal(arr_trans[,, "beta[1]"], exp(arr[,, "beta[1]"]))
 
-  mat_trans <- apply_transformations(mat, trans)
+  mat_trans <- apply_transformations(mat, transformations = trans)
   expect_equal(mat_trans[, "sigma"], mat[, "sigma"]^2)
   expect_equal(mat_trans[, "beta[1]"], exp(mat[, "beta[1]"]))
 })

vignettes/visual-mcmc-diagnostics.Rmd

@@ -154,7 +154,7 @@ schools_mod_ncp <- stan_model("schools_mod_ncp.stan")
 We then fit the model by calling Stan's MCMC algorithm using the `sampling` 
 function (the increased `adapt_delta` param is to make the sampler a bit more "careful" and avoid false positive divergences),
 ```{r fit-models-hidden, results='hide', message=FALSE}
-fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214054, control = list(adapt_delta = 0.9))
+fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214055, control = list(adapt_delta = 0.9))
 fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9))
 ```
 and extract a `iterations x chains x parameters` array of posterior draws with