change threads for data.table from 1 to 2

R/avail_forecasts.R

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

tests/testthat/setup.R

@@ -1,7 +1,7 @@
 # load common required test packages
 library(ggplot2, quietly = TRUE)
 suppressMessages(library(magrittr))
-data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+data.table::setDTthreads(2) # only needed to avoid issues on CRAN
 
 # compute quantile scores
 scores <- suppressMessages(score(example_quantile))

vignettes/scoringutils.Rmd

@@ -19,7 +19,7 @@ library(magrittr)
 library(data.table)
 library(ggplot2)
 library(knitr)
-data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+data.table::setDTthreads(2) # only needed to avoid issues 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).
@@ -36,7 +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(