delete validate_forecast()

NAMESPACE

@@ -92,7 +92,6 @@ export(theme_scoringutils)
 export(transform_forecasts)
 export(underprediction_quantile)
 export(underprediction_sample)
-export(validate_forecast)
 export(wis)
 importFrom(Metrics,ae)
 importFrom(Metrics,ape)

NEWS.md

@@ -50,7 +50,7 @@ of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` pape
     - The function `interval_score()` was made an internal function rather than being exported to users. We recommend using `wis()` instead. 
 
 ### (Re-)Validating forecast objects
-- To create and validate a new `forecast` object, users can use `as_forecast_<type>()`. To revalidate an existing `forecast` object users can call `assert_forecast()` (which validates the input and returns `invisible(NULL)`. `assert_forecast()` is a generic with methods for the different forecast types. Alternatively, `validate_forecast()` can be used (which calls `assert_forecast()`), which returns the input and is useful in a pipe. Lastly, users can simply print the object to obtain additional information. 
+- To create and validate a new `forecast` object, users can use `as_forecast_<type>()`. To revalidate an existing `forecast` object users can call `assert_forecast()` (which validates the input and returns `invisible(NULL)`. `assert_forecast()` is a generic with methods for the different forecast types. Alternatively, users can call ``as_forecast_<type>()` again to re-validate a forecast object. Simply printing the object will also provide some additional information. 
 - Users can test whether an object is of class `forecast_*()` using the function `is_forecast()`. Users can also test for a specific `forecast_*` class using the appropriate `is_forecast.forecast_*` method. For example, to check whether an object is of class `forecast_quantile`, you would use you would use `scoringutils:::is_forecast.forecast_quantile()`.
 
 ### Pairwise comparisons and relative skill

R/forecast.R

@@ -502,21 +502,6 @@ assert_forecast.forecast_nominal <- function(
 }
 
 
-#' @title Re-validate an existing forecast object
-#'
-#' @description
-#' The function re-validates an existing forecast object. It is similar to
-#' [assert_forecast()], but returns the input data instead of an invisible
-#' `NULL`. See [as_forecast()] for details on the expected input formats.
-#' @inherit assert_forecast params return examples
-#' @export
-#' @keywords validate-forecast-object
-validate_forecast <- function(forecast, forecast_type = NULL, verbose = TRUE) {
-  assert_forecast(forecast, forecast_type, verbose)
-  return(forecast)
-}
-
-
 #' @title Validation common to all forecast types
 #'
 #' @description

tests/testthat/test-forecast.R

@@ -278,24 +278,6 @@ test_that("assert_forecast_generic() works as expected with a data.frame", {
 })
 
 
-# ==============================================================================
-# validate_forecast()
-# ==============================================================================
-
-test_that("validate_forecast() works as expected", {
-  # check that validate forecast returns itself
-  expect_no_condition(
-    out <- validate_forecast(as_forecast_point(na.omit(example_point)))
-  )
-  expect_true(!is.null(out))
-
-  expect_equal(
-    validate_forecast(as_forecast_point(na.omit(example_point))),
-    as_forecast_point(na.omit(example_point))
-  )
-})
-
-
 # ==============================================================================
 # new_forecast()
 # ==============================================================================