Fix refund deprecation warnings and stabilize checks

Author: davidruegamer

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: FDboost
 Title: Boosting Functional Regression Models
-Version: 1.1-1
-Date: 2022-09-08
+Version: 1.1-4
+Date: 2026-03-24
 Authors@R: c(
     person("Sarah", "Brockhaus", , "Sarah.Brockhaus@stat.uni-muenchen.de", role = "aut",
            comment = c(ORCID = "0000-0001-9484-7488")),

NEWS.md

@@ -1,3 +1,10 @@
+# FDboost 1.1-4 (2026-03-24)
+
+## Bug fixes
+
+- Suppressed deprecation warnings from `refund::pffrSim()` in examples to keep checks clean with recent `refund` versions.
+- Stabilized `tests/general_tests.R` by forcing serial evaluation (`mc.cores = 1`) to avoid parallel `applyFolds()` crashes on some check platforms.
+
 # FDboost 1.1.0 (2022-07-12)
 
 ## Miscellaneous

R/baselearners.R

@@ -585,7 +585,7 @@ X_bsignal <- function(mf, vary, args) {
 #' # model with linear functional effect, use bsignal()
 #' # Y(t) = f(t) + \int X1(s)\beta(s,t)ds + eps
 #' set.seed(2121)
-#' data1 <- pffrSim(scenario = "ff", n = 40)
+#' data1 <- suppressWarnings(pffrSim(scenario = "ff", n = 40))
 #' data1$X1 <- scale(data1$X1, scale = FALSE)
 #' dat_list <- as.list(data1)
 #' dat_list$t <- attr(data1, "yindex")
@@ -623,7 +623,7 @@ X_bsignal <- function(mf, vary, args) {
 #' mylimits <- function(s, t){
 #'   (s < t) | (s == t)
 #' }
-#' data2 <- pffrSim(scenario = "ff", n = 40, limits = mylimits)
+#' data2 <- suppressWarnings(pffrSim(scenario = "ff", n = 40, limits = mylimits))
 #' data2$X1 <- scale(data2$X1, scale = FALSE)
 #' dat2_list <- as.list(data2)
 #' dat2_list$t <- attr(data2, "yindex")
@@ -2600,4 +2600,3 @@ brandomc <- function (..., contrasts.arg = "contr.dummy", df = 4) {
   ret
 }
 
-

R/baselearnersX.R

@@ -395,8 +395,8 @@ X_histx <- function(mf, vary, args) {
 #' ## the interaction effect is in this case not necessary
 #' n <- 100
 #' nygrid <- 35
-#' data1 <- pffrSim(scenario = c("int", "ff"), limits = function(s,t){ s <= t }, 
-#'                 n = n, nygrid = nygrid)
+#' data1 <- suppressWarnings(pffrSim(scenario = c("int", "ff"), limits = function(s,t){ s <= t }, 
+#'                 n = n, nygrid = nygrid))
 #' data1$X1 <- scale(data1$X1, scale = FALSE) ## center functional covariate                  
 #' dataList <- as.list(data1)
 #' dataList$tvals <- attr(data1, "yindex")

R/bootstrapCIs.R

@@ -47,7 +47,7 @@
 #' 
 #' @note Note that parallelization can be achieved by defining
 #' the \code{resampling_fun_outer} or \code{_inner} accordingly.
-#' See, e.g., \code{\link{cvrisk}} on how to parallelize resampling
+#' See, e.g., \code{\link[mboost]{cvrisk}} on how to parallelize resampling
 #' functions or the examples below. Also note that by defining
 #' a custum inner or outer resampling function the respective
 #' argument \code{B_inner} or \code{B_outer} is ignored.
@@ -77,7 +77,7 @@
 #' # model with linear functional effect, use bsignal()
 #' # Y(t) = f(t) + \int X1(s)\beta(s,t)ds + eps
 #' set.seed(2121)
-#' data1 <- pffrSim(scenario = "ff", n = 40)
+#' data1 <- suppressWarnings(pffrSim(scenario = "ff", n = 40))
 #' data1$X1 <- scale(data1$X1, scale = FALSE)
 #' dat_list <- as.list(data1)
 #' dat_list$t <- attr(data1, "yindex")

man/bhistx.Rd

@@ -10,10 +10,14 @@ library(gamboostLSS)
 
 if(require(refund)){
 
+  old_mc_cores <- getOption("mc.cores")
+  options(mc.cores = 1L)
+  on.exit(options(mc.cores = old_mc_cores), add = TRUE)
+  
   ## simulate a small data set 
   print("simulate data")
   set.seed(230)
-  pffr_data <- pffrSim(n = 25, nxgrid = 21, nygrid = 19)
+  pffr_data <- suppressWarnings(pffrSim(n = 25, nxgrid = 21, nygrid = 19))
   pffr_data$X1 <- scale(pffr_data$X1, scale = FALSE)
 
   dat <- as.list(pffr_data)
@@ -210,4 +214,3 @@ pred <- predict(fof, newdata = fuelSubset)
 
 
 
-