Merge branch 'develop' into issue555

epiforecasts · Jan 5, 2024 · 3c57faf · 3c57faf
2 parents 15283dd + 8796b9f
commit 3c57faf
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diff --git a/.github/workflows/render_readme.yaml b/.github/workflows/render_readme.yaml
@@ -11,6 +11,7 @@ on:
   push:
     paths:
       - 'README.Rmd'
+      - DESCRIPTION
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: scoringutils
 Title: Utilities for Scoring and Assessing Predictions
-Version: 1.2.2
+Version: 1.2.2.9000
 Language: en-GB
 Authors@R: c(
     person(given = "Nikos",

diff --git a/NAMESPACE b/NAMESPACE
@@ -13,7 +13,6 @@ S3method(validate_forecast,forecast_binary)
 S3method(validate_forecast,forecast_point)
 S3method(validate_forecast,forecast_quantile)
 S3method(validate_forecast,forecast_sample)
-export(abs_error)
 export(add_coverage)
 export(add_pairwise_comparison)
 export(ae_median_quantile)
@@ -68,7 +67,6 @@ export(score)
 export(se_mean_sample)
 export(select_rules)
 export(set_forecast_unit)
-export(squared_error)
 export(summarise_scores)
 export(summarize_scores)
 export(theme_scoringutils)

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# scoringutils 2.0.0
+# scoringutils 1.2.2.9000
 
 This major update and addresses a variety of comments made by reviewers from the Journal of Statistical Software (see preprint of the manuscript [here](https://arxiv.org/abs/2205.07090)).
 
@@ -36,10 +36,12 @@ The update introduces breaking changes. If you want to keep using the older vers
   - `plot_avail_forecasts()` was renamed `plot_forecast_counts()` in line with the change in the function name. The `x` argument no longer has a default value, as the value will depend on the data provided by the user.
 - The deprecated `..density..` was replaced with `after_stat(density)` in ggplot calls.
 - Files ending in ".Rda" were renamed to ".rds" where appropriate when used together with `saveRDS()` or `readRDS()`.
-- `score()` now calls `na.omit()` on the data, instead of only removing rows with missing values in the columns `observed` and `predicted`. This is because `NA` values in other columns can also mess up e.g. grouping of forecasts according to the unit of a single forecast. 
+- `score()` now calls `na.omit()` on the data, instead of only removing rows with missing values in the columns `observed` and `predicted`. This is because `NA` values in other columns can also mess up e.g. grouping of forecasts according to the unit of a single forecast.
 - added documentation for the return value of `summarise_scores()`.
+- Removed abs_error and squared_error from the package in favour of `Metrics::ae` and `Metrics::se`. 
 - Renamed `interval_coverage_quantile()` and `interval_coverage_dev_quantile()` to `interval_coverage()` and `interval_coverage_deviation()`, respectively. Removed `interval_coverage_sample()` as users are now expected to convert to a quantile format first before scoring.
-
+- Added unit tests for `interval_coverage_quantile()` and `interval_coverage_dev_quantile()` in order to make sure that the functions provide the correct warnings when insufficient quantiles are provided.
+- Documentation pkgdown pages are now created both for the stable and dev versions.
 
 # scoringutils 1.2.2
 

diff --git a/R/metrics-point.R b/R/metrics-point.R
diff --git a/R/metrics-quantile.R b/R/metrics-quantile.R
@@ -499,7 +499,7 @@ bias_quantile_single_vector <- function(observed, predicted, quantile, na.rm) {
 #' `quantile`.
 #' @inheritParams wis
 #' @return numeric vector of length N with the absolute error of the median
-#' @seealso [ae_median_sample()], [abs_error()]
+#' @seealso [ae_median_sample()]
 #' @importFrom stats median
 #' @examples
 #' observed <- rnorm(30, mean = 1:30)

diff --git a/R/metrics-sample.R b/R/metrics-sample.R
@@ -92,7 +92,7 @@ bias_sample <- function(observed, predicted) {
 #' the number of data points and N (number of columns) the number of Monte
 #' Carlo samples. Alternatively, `predicted` can just be a vector of size n.
 #' @return vector with the scoring values
-#' @seealso [ae_median_quantile()], [abs_error()]
+#' @seealso [ae_median_quantile()]
 #' @importFrom stats median
 #' @examples
 #' observed <- rnorm(30, mean = 1:30)
@@ -127,7 +127,6 @@ ae_median_sample <- function(observed, predicted) {
 #' the number of data points and N (number of columns) the number of Monte
 #' Carlo samples. Alternatively, `predicted` can just be a vector of size n.
 #' @return vector with the scoring values
-#' @seealso [squared_error()]
 #' @examples
 #' observed <- rnorm(30, mean = 1:30)
 #' predicted_values <- matrix(rnorm(30, mean = 1:30))

diff --git a/README.Rmd b/README.Rmd
@@ -26,6 +26,31 @@ library(magrittr)
 library(data.table)
 library(ggplot2)
 library(knitr)
+
+## code to determine version inspired by [pkgdown:::dev_mode_auto()]
+version <- packageVersion("scoringutils")
+devel <- length(unclass(package_version(version))[[1]]) > 3
+```
+
+```{r note_dev, results = 'asis', echo = FALSE}
+if (devel) {
+  cat(
+    "**Note**: ",
+    "This documentation refers to the development version of `scoringutils`. ",
+    "You can also view the [documentation of the stable version]",
+    "(https://epiforecasts.io/scoringutils).",
+    sep = ""
+  )
+} else {
+  cat(
+    "**Note**: ",
+    "This documentation refers to the stable version of `scoringutils`. ",
+    "You can also view the [documentation of the development version]",
+    "(https://epiforecasts.io/scoringutils/dev).",
+    sep = ""
+  )
+}
+cat("\n\n")
 ```
 
 The `scoringutils` package provides a collection of metrics and proper scoring rules and aims to make it simple to score probabilistic forecasts against observed values. 

diff --git a/man/abs_error.Rd b/man/abs_error.Rd
diff --git a/man/ae_median_quantile.Rd b/man/ae_median_quantile.Rd
diff --git a/man/ae_median_sample.Rd b/man/ae_median_sample.Rd
diff --git a/man/se_mean_sample.Rd b/man/se_mean_sample.Rd
diff --git a/man/squared_error.Rd b/man/squared_error.Rd
diff --git a/tests/testthat/test-metrics-quantile.R b/tests/testthat/test-metrics-quantile.R
@@ -601,6 +601,17 @@ test_that("interval_coverage rejects wrong inputs", {
   )
 })
 
+test_that("interval_coverage_quantile throws a warning when a required quantile is not available", {
+  dropped_quantile_pred <- predicted[, -4]
+  dropped_quantiles <- quantile[-4]
+  expect_warning(
+    interval_coverage_quantile(
+      observed, dropped_quantile_pred, dropped_quantiles, range = 50
+    ),
+    "To compute the interval coverage for a range of 50%, the quantiles `0.25, 0.75` are required. Returning `NA`"
+  )
+})
+
 
 # ============================================================================ #
 # `interval_coverage_deviation` ===================================== #
@@ -617,6 +628,12 @@ test_that("interval_coverage_deviation works", {
     interval_coverage_deviation(observed, predicted, quantile),
     manual
   )
+  expect_warning(
+    interval_coverage_dev_quantile(
+      observed, predicted, c(quantile[-4], 0.76)
+    ),
+    "To compute inteval coverage deviation, all quantiles must form central symmetric prediction intervals. Missing quantiles: 0.24, 0.75. Returning `NA`."
+  )
 })