Increase test coverage

.Rbuildignore

@@ -19,3 +19,4 @@
 ^CODE_OF_CONDUCT\.md$
 ^inst/manuscript/output$
 ^CRAN-SUBMISSION$
+^.vscode

NAMESPACE

@@ -118,10 +118,8 @@ importFrom(checkmate,check_numeric)
 importFrom(checkmate,check_set_equal)
 importFrom(checkmate,check_vector)
 importFrom(checkmate,test_atomic_vector)
-importFrom(checkmate,test_factor)
 importFrom(checkmate,test_list)
 importFrom(checkmate,test_names)
-importFrom(checkmate,test_numeric)
 importFrom(checkmate,test_set_equal)
 importFrom(checkmate,test_subset)
 importFrom(cli,cli_abort)

R/get_-functions.R

@@ -19,70 +19,6 @@ get_forecast_type <- function(forecast) {
 }
 
 
-#' Test whether data could be a binary forecast.
-#' @description Checks type of the necessary columns.
-#' @inheritParams document_check_functions
-#' @importFrom checkmate test_factor test_numeric
-#' @return Returns TRUE if basic requirements are satisfied and FALSE otherwise
-#' @keywords internal_input_check
-test_forecast_type_is_binary <- function(data) {
-  observed_correct <- test_factor(x = data$observed)
-  predicted_correct <- test_numeric(x = data$predicted)
-  return(observed_correct && predicted_correct)
-}
-
-#' Test whether data could be a sample-based forecast.
-#' @description Checks type of the necessary columns.
-#' @inheritParams document_check_functions
-#' @return Returns TRUE if basic requirements are satisfied and FALSE otherwise
-#' @keywords internal_input_check
-test_forecast_type_is_sample <- function(data) {
-  observed_correct <- test_numeric(x = data$observed)
-  predicted_correct <- test_numeric(x = data$predicted)
-  columns_correct <- test_columns_present(data, "sample_id")
-  return(observed_correct && predicted_correct && columns_correct)
-}
-
-#' Test whether data could be a point forecast.
-#' @description Checks type of the necessary columns.
-#' @inheritParams document_check_functions
-#' @return Returns TRUE if basic requirements are satisfied and FALSE otherwise
-#' @keywords internal_input_check
-test_forecast_type_is_point <- function(data) {
-  observed_correct <- test_numeric(x = data$observed)
-  predicted_correct <- test_numeric(x = data$predicted)
-  columns_correct <- test_columns_not_present(
-    data, c("sample_id", "quantile_level")
-  )
-  return(observed_correct && predicted_correct && columns_correct)
-}
-
-#' Test whether data could be a quantile forecast.
-#' @description Checks type of the necessary columns.
-#' @inheritParams document_check_functions
-#' @return Returns TRUE if basic requirements are satisfied and FALSE otherwise
-#' @keywords internal_input_check
-test_forecast_type_is_quantile <- function(data) {
-  observed_correct <- test_numeric(x = data$observed)
-  predicted_correct <- test_numeric(x = data$predicted)
-  columns_correct <- test_columns_present(data, "quantile_level")
-  return(observed_correct && predicted_correct && columns_correct)
-}
-
-#' Test whether data could be a nominal forecast.
-#' @description Checks type of the necessary columns.
-#' @inheritParams document_check_functions
-#' @return Returns TRUE if basic requirements are satisfied and FALSE otherwise
-#' @keywords internal_input_check
-test_forecast_type_is_nominal <- function(data) {
-  observed_correct <- test_factor(x = data$observed)
-  predicted_correct <- test_numeric(x = data$predicted)
-  columns_correct <- test_columns_present(data, "predicted_label")
-  predicted_label_correct <- test_factor(x = data$predicted_label)
-  return(observed_correct && predicted_correct &&
-           columns_correct && predicted_label_correct)
-}
-
 #' Assert that forecast type is as expected
 #' @param data A forecast object (see [as_forecast()]).
 #' @param actual The actual forecast type of the data