Merge pull request #998 from epiforecasts/multivariate

Author: seabbs

DESCRIPTION

@@ -75,7 +75,7 @@ Config/Needs/website:
     r-lib/pkgdown,
     amirmasoudabdol/preferably
 Config/testthat/edition: 3
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 URL: https://doi.org/10.48550/arXiv.2205.07090, https://epiforecasts.io/scoringutils/, https://github.com/epiforecasts/scoringutils
 BugReports: https://github.com/epiforecasts/scoringutils/issues
 VignetteBuilder: knitr

NAMESPACE

@@ -6,6 +6,7 @@ S3method("[<-",forecast)
 S3method("[[<-",forecast)
 S3method(`[`,scores)
 S3method(as_forecast_binary,default)
+S3method(as_forecast_multivariate_sample,default)
 S3method(as_forecast_nominal,default)
 S3method(as_forecast_ordinal,default)
 S3method(as_forecast_point,default)
@@ -20,12 +21,14 @@ S3method(assert_forecast,forecast_ordinal)
 S3method(assert_forecast,forecast_point)
 S3method(assert_forecast,forecast_quantile)
 S3method(assert_forecast,forecast_sample)
+S3method(assert_forecast,forecast_sample_multivariate)
 S3method(get_metrics,forecast_binary)
 S3method(get_metrics,forecast_nominal)
 S3method(get_metrics,forecast_ordinal)
 S3method(get_metrics,forecast_point)
 S3method(get_metrics,forecast_quantile)
 S3method(get_metrics,forecast_sample)
+S3method(get_metrics,forecast_sample_multivariate)
 S3method(get_metrics,scores)
 S3method(get_pit_histogram,default)
 S3method(get_pit_histogram,forecast_quantile)
@@ -39,11 +42,13 @@ S3method(score,forecast_ordinal)
 S3method(score,forecast_point)
 S3method(score,forecast_quantile)
 S3method(score,forecast_sample)
+S3method(score,forecast_sample_multivariate)
 S3method(tail,forecast)
 export(add_relative_skill)
 export(ae_median_quantile)
 export(ae_median_sample)
 export(as_forecast_binary)
+export(as_forecast_multivariate_sample)
 export(as_forecast_nominal)
 export(as_forecast_ordinal)
 export(as_forecast_point)
@@ -57,11 +62,13 @@ export(crps_sample)
 export(dispersion_quantile)
 export(dispersion_sample)
 export(dss_sample)
+export(energy_score_multivariate)
 export(get_correlations)
 export(get_coverage)
 export(get_duplicate_forecasts)
 export(get_forecast_counts)
 export(get_forecast_unit)
+export(get_grouping)
 export(get_metrics)
 export(get_pairwise_comparisons)
 export(get_pit_histogram)
@@ -73,6 +80,7 @@ export(is_forecast_ordinal)
 export(is_forecast_point)
 export(is_forecast_quantile)
 export(is_forecast_sample)
+export(is_forecast_sample_multivariate)
 export(log_shift)
 export(logs_binary)
 export(logs_categorical)
@@ -137,6 +145,7 @@ importFrom(cli,cli_inform)
 importFrom(cli,cli_text)
 importFrom(cli,cli_warn)
 importFrom(cli,col_blue)
+importFrom(cli,qty)
 importFrom(data.table,"%like%")
 importFrom(data.table,':=')
 importFrom(data.table,.I)
@@ -148,12 +157,14 @@ importFrom(data.table,data.table)
 importFrom(data.table,dcast)
 importFrom(data.table,fcase)
 importFrom(data.table,is.data.table)
+importFrom(data.table,key)
 importFrom(data.table,melt)
 importFrom(data.table,nafill)
 importFrom(data.table,rbindlist)
 importFrom(data.table,setDT)
 importFrom(data.table,setattr)
 importFrom(data.table,setcolorder)
+importFrom(data.table,setkeyv)
 importFrom(data.table,setnames)
 importFrom(data.table,setorderv)
 importFrom(ggplot2,.data)
@@ -188,10 +199,12 @@ importFrom(ggplot2,theme_minimal)
 importFrom(ggplot2,unit)
 importFrom(ggplot2,xlab)
 importFrom(ggplot2,ylab)
+importFrom(methods,formalArgs)
 importFrom(methods,hasArg)
 importFrom(purrr,partial)
 importFrom(scoringRules,crps_sample)
 importFrom(scoringRules,dss_sample)
+importFrom(scoringRules,es_sample)
 importFrom(scoringRules,logs_sample)
 importFrom(scoringRules,rps_probs)
 importFrom(stats,cor)

NEWS.md

@@ -1,5 +1,7 @@
 # scoringutils (development version)
 
+- Added support for scoring multivariate forecasts (#288, big thank you to Sam Abbott and Sebastian Funk). You can find detailed information in the Vignette "Scoring multivariate forecasts". There is a new forecast type, `forecast_multivariate_sample()` and a corresponding `as_forecast_multivariate_sample()` function. To score a multivariate forecast, users are expected to provide a `joint_across` argument which specifies the variables which are forecast jointly. 
+
 # scoringutils 2.1.2
 
 - fixed an issue that could arise with small rounding errors in quantile-based forecasts. This happened when there were quantile_levels like 0.5, and 0.5 + 1e-16 present at the same time. `scoringutils` now warns the user of the issue and automatically rounds all quantile levels to 10 digits. 
@@ -156,7 +158,7 @@ This major release contains a range of new features and bug fixes that have been
 
 - Documentation updated to reflect changes since version 1.1.0, including new transform and workflow functions.
 - New `set_forecast_unit()` function allows manual setting of forecast unit.
-- `summarise_scores()` gains new `across` argument for summarizing across variables.
+- `summarise_scores()` gains new `across` argument for summarizing across variables. EDIT: This has since been removed again in [PR #831](https://github.com/epiforecasts/scoringutils/pull/831).
 - New `transform_forecasts()` and `log_shift()` functions allow forecast transformations. See the documentation for `transform_forecasts()` for more details and an example use case.
 - Input checks and test coverage improved for bias functions.
 - Bug fix in `get_prediction_type()` for integer matrix input.

R/class-forecast-binary.R

@@ -1,9 +1,10 @@
 #' @title Create a `forecast` object for binary forecasts
 #' @inherit as_forecast_doc_template params description
 #' @details
-#' # Required input
+#' # Target format
 #'
-#' The input needs to be a data.frame or similar with the following columns:
+#' The input for all further scoring needs to be a data.frame or similar with
+#' the following columns:
 #' - `observed`: `factor` with exactly two levels representing the observed
 #'   values. The highest factor level is assumed to be the reference level.
 #'   This means that corresponding value in `predicted` represent the