Issue 486: Fix NOTEs for CRAN submission

.Rbuildignore

@@ -17,3 +17,4 @@
 ^\.devcontainer$
 ^CODE_OF_CONDUCT\.md$
 ^inst/manuscript/output$
+^CRAN-SUBMISSION$

R/avail_forecasts.R

@@ -27,7 +27,9 @@
 #' @export
 #' @keywords check-forecasts
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' avail_forecasts(example_quantile,
 #'   collapse = c("quantile"),

R/pairwise-comparisons.R

@@ -52,7 +52,9 @@
 #' @author Johannes Bracher, \email{johannes.bracher@@kit.edu}
 #' @keywords scoring
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' scores <- score(example_quantile)
 #' pairwise <- pairwise_comparison(scores, by = "target_type")
@@ -227,8 +229,8 @@
 
   # make result character instead of factor
   result[, `:=`(
     "model" = as.character(model),
     "compare_against" = as.character(compare_against)
   )]
 
   # calculate relative skill as geometric mean

R/pit.R

@@ -62,7 +62,9 @@
 #' @seealso [pit()]
 #' @importFrom stats runif
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' ## continuous predictions
 #' true_values <- rnorm(20, mean = 1:20)
@@ -125,7 +127,7 @@
 
   # check data type ------------------------------------------------------------
   # check whether continuous or integer
   if (!isTRUE(all.equal(as.vector(predictions), as.integer(predictions)))) {
     continuous_predictions <- TRUE
   } else {
     continuous_predictions <- FALSE
@@ -217,7 +219,7 @@
     value.var = "prediction"
   )
 
   pit <- data_wide[, .("pit_value" = pit_sample(
     true_values = true_value,
     predictions = as.matrix(.SD)
   )),

R/plot.R

@@ -24,7 +24,9 @@
 #' @examples
 #' library(ggplot2)
 #' library(magrittr) # pipe operator
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' scores <- score(example_quantile) %>%
 #'   summarise_scores(by = c("model", "target_type")) %>%
@@ -470,7 +472,7 @@
   # it separately here to deal with the case when only the median is provided
   # (in which case ggdist::geom_lineribbon() will fail)
   if (0 %in% range) {
     select_median <- (forecasts$range %in% 0 & forecasts$boundary == "lower")
     median <- forecasts[select_median]
 
     if (nrow(median) > 0) {
@@ -582,7 +584,9 @@
 #' @importFrom data.table dcast
 #' @export
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #' scores <- score(example_quantile)
 #' scores <- summarise_scores(scores, by = c("model", "range"))
 #' plot_interval_coverage(scores)
@@ -835,7 +839,9 @@
 #' @importFrom stats density
 #' @return vector with the scoring values
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' # PIT histogram in vector based format
 #' true_values <- rnorm(30, mean = 1:30)

R/score.R

@@ -75,7 +75,9 @@
 #'
 #' @examples
 #' library(magrittr) # pipe operator
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' check_forecasts(example_quantile)
 #' score(example_quantile) %>%

R/summarise_scores.R

@@ -42,7 +42,9 @@
 #' provided to `fun`. For more information see the documentation of the
 #' respective function.
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #' library(magrittr) # pipe operator
 #'
 #' scores <- score(example_continuous)
@@ -279,6 +281,9 @@ check_summary_params <- function(scores,
 #' summary is present according to the value specified in `by`.
 #' @examples
 #' library(magrittr) # pipe operator
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #' score(example_quantile) %>%
 #'   add_coverage(by = c("model", "target_type")) %>%
 #'   summarise_scores(by = c("model", "target_type")) %>%

tests/testthat/setup.R

@@ -1,6 +1,7 @@
 # load common required test packages
 library(ggplot2, quietly = TRUE)
 suppressMessages(library(magrittr))
+data.table::setDTthreads(2) # restricts number of cores used on CRAN
 
 # compute quantile scores
-scores <- suppressMessages(score(example_quantile))
+scores <- suppressMessages(score(example_quantile))

vignettes/scoringutils.Rmd

@@ -19,6 +19,7 @@ library(magrittr)
 library(data.table)
 library(ggplot2)
 library(knitr)
+data.table::setDTthreads(2) # restricts number of cores used on CRAN
 ```
 
 The `scoringutils` package provides a collection of metrics and proper scoring rules that make it simple to score probabilistic forecasts against the true observed values. You can find more information in the paper [Evaluating Forecasts with scoringutils in R](https://arxiv.org/abs/2205.07090) as well as the [Metrics-Vignette](https://epiforecasts.io/scoringutils/articles/metric-details.html) and the [Scoring forecasts directly Vignette](https://epiforecasts.io/scoringutils/articles/scoring-forecasts-directly.html).
@@ -35,7 +36,7 @@ Most of the time, the `score()` function will be able to do the entire evaluatio
 
 ```{r, echo=FALSE}
 requirements <- data.table(
-  "Format" = c(
+  Format = c(
     "quantile-based", "sample-based", "binary", "pairwise-comparisons"
   ),
   `Required columns` = c(