| Add relative skill scores based on pairwise comparisons | add_relative_skill |
| Absolute error of the median (quantile-based version) | ae_median_quantile |
| Absolute error of the median (sample-based version) | ae_median_sample |
| Create a 'forecast' object for binary forecasts | as_forecast_binary as_forecast_binary.default |
| General information on creating a 'forecast' object | as_forecast_doc_template |
| Common functionality for as_forecast_<type> functions | as_forecast_generic |
| Create a 'forecast' object for multivariate point forecasts | as_forecast_multivariate_point as_forecast_multivariate_point.default |
| Create a 'forecast' object for sample-based multivariate forecasts | as_forecast_multivariate_sample as_forecast_multivariate_sample.default |
| Create a 'forecast' object for nominal forecasts | as_forecast_nominal as_forecast_nominal.default |
| Create a 'forecast' object for ordinal forecasts | as_forecast_ordinal as_forecast_ordinal.default |
| Create a 'forecast' object for point forecasts | as_forecast_point as_forecast_point.default as_forecast_point.forecast_quantile |
| Create a 'forecast' object for quantile-based forecasts | as_forecast_quantile as_forecast_quantile.default as_forecast_quantile.forecast_sample |
| Create a 'forecast' object for sample-based forecasts | as_forecast_sample as_forecast_sample.default |
| Assert Inputs Have Matching Dimensions | assert_dims_ok_scalar |
| Validation common to all forecast types | assert_forecast_generic |
| Assert that forecast type is as expected | assert_forecast_type |
| Assert that input is a forecast object and passes validations | assert_forecast assert_forecast.default assert_forecast.forecast_binary assert_forecast.forecast_multivariate_point assert_forecast.forecast_multivariate_sample assert_forecast.forecast_point assert_forecast.forecast_quantile assert_forecast.forecast_sample |
| Assert that inputs are correct for binary forecast | assert_input_binary |
| Assert that inputs are correct for categorical forecasts | assert_input_categorical |
| Assert that inputs are correct for interval-based forecast | assert_input_interval |
| Assert that inputs are correct for sample-based forecast | assert_input_multivariate_sample |
| Assert that inputs are correct for nominal forecasts | assert_input_nominal |
| Assert that inputs are correct for ordinal forecasts | assert_input_ordinal |
| Assert that inputs are correct for point forecast | assert_input_point |
| Assert that inputs are correct for quantile-based forecast | assert_input_quantile |
| Assert that inputs are correct for sample-based forecast | assert_input_sample |
| Determines bias of quantile forecasts | bias_quantile |
| Determine bias of forecasts | bias_sample |
| Check Inputs Have Matching Dimensions | check_dims_ok_scalar |
| Check that there are no duplicate forecasts | check_duplicates |
| Check that inputs are correct for binary forecast | check_input_binary |
| Check that inputs are correct for point forecast | check_input_point |
| Check that all forecasts have the same number of rows | check_number_per_forecast |
| Check whether an input is an atomic vector of mode 'numeric' | check_numeric_vector |
| Helper function to convert assert statements into checks | check_try |
| (Continuous) ranked probability score | crps_sample dispersion_sample overprediction_sample underprediction_sample |
| Dawid-Sebastiani score | dss_sample |
| Energy score for multivariate forecasts | energy_score_multivariate |
| Binary forecast example data | example_binary |
| Multivariate forecast example data | example_multivariate_sample |
| Nominal example data | example_nominal |
| Ordinal example data | example_ordinal |
| Point forecast example data | example_point |
| Quantile example data | example_quantile |
| Continuous forecast example data | example_sample_continuous |
| Discrete forecast example data | example_sample_discrete |
| Calculate correlation between metrics | get_correlations |
| Get quantile and interval coverage values for quantile-based forecasts | get_coverage |
| Find duplicate forecasts | get_duplicate_forecasts |
| Count number of available forecasts | get_forecast_counts |
| Get forecast type from forecast object | get_forecast_type |
| Get type-specific ID columns for a forecast | get_forecast_type_ids get_forecast_type_ids.forecast_multivariate_sample get_forecast_type_ids.forecast_nominal get_forecast_type_ids.forecast_ordinal get_forecast_type_ids.forecast_quantile get_forecast_type_ids.forecast_sample |
| Get unit of a single forecast | get_forecast_unit |
| Get grouping for a multivariate forecast | get_grouping |
| Get metrics | get_metrics |
| Get default metrics for binary forecasts | get_metrics.forecast_binary |
| Get default metrics for multivariate point forecasts | get_metrics.forecast_multivariate_point |
| Get default metrics for sample-based forecasts | get_metrics.forecast_multivariate_sample |
| Get default metrics for nominal forecasts | get_metrics.forecast_nominal |
| Get default metrics for nominal forecasts | get_metrics.forecast_ordinal |
| Get default metrics for point forecasts | get_metrics.forecast_point |
| Get default metrics for quantile-based forecasts | get_metrics.forecast_quantile |
| Get default metrics for sample-based forecasts | get_metrics.forecast_sample |
| Get names of the metrics that were used for scoring | get_metrics.scores |
| Obtain pairwise comparisons between models | get_pairwise_comparisons |
| Probability integral transformation histogram | get_pit_histogram get_pit_histogram.default get_pit_histogram.forecast_quantile get_pit_histogram.forecast_sample |
| Get type of a vector or matrix of observed values or predictions | get_type |
| Interval coverage (for quantile-based forecasts) | interval_coverage |
| Interval score | interval_score |
| Test whether an object is a forecast object | is_forecast is_forecast_binary is_forecast_multivariate_point is_forecast_multivariate_sample is_forecast_nominal is_forecast_ordinal is_forecast_point is_forecast_quantile is_forecast_sample |
| Log transformation with an additive shift | log_shift |
| Log score for categorical outcomes | logs_categorical |
| Logarithmic score (sample-based version) | logs_sample |
| Determine dispersion of a probabilistic forecast | mad_sample |
| Probability integral transformation for counts | pit_histogram_sample |
| Plot correlation between metrics | plot_correlations |
| Plot discrimination for binary forecasts | plot_discrimination |
| Visualise the number of available forecasts | plot_forecast_counts |
| Create a heatmap of a scoring metric | plot_heatmap |
| Plot interval coverage | plot_interval_coverage |
| Plot heatmap of pairwise comparisons | plot_pairwise_comparisons |
| Plot quantile coverage | plot_quantile_coverage |
| Plot contributions to the weighted interval score | plot_wis |
| Print information about a forecast object | print.forecast |
| Print a scores object | print.scores |
| Quantile score | quantile_score |
| Ranked Probability Score for ordinal outcomes | rps_ordinal |
| Evaluate forecasts | score score.forecast_binary score.forecast_multivariate_point score.forecast_multivariate_sample score.forecast_nominal score.forecast_ordinal score.forecast_point score.forecast_quantile score.forecast_sample |
| Metrics for binary outcomes | brier_score logs_binary scoring-functions-binary |
| Squared error of the mean (sample-based version) | se_mean_sample |
| Select metrics from a list of functions | select_metrics |
| Set unit of a single forecast manually | set_forecast_unit |
| Summarise scores as produced by 'score()' | summarise_scores summarize_scores |
| Scoringutils ggplot2 theme | theme_scoringutils |
| Transform forecasts and observed values | transform_forecasts transform_forecasts.default transform_forecasts.forecast transform_forecasts.forecast_multivariate_point transform_forecasts.forecast_multivariate_sample |
| Validate metrics | validate_metrics |
| Variogram score for multivariate forecasts | variogram_score_multivariate |
| Variogram score for multivariate point forecasts | variogram_score_multivariate_point |
| Weighted interval score (WIS) | dispersion_quantile overprediction_quantile underprediction_quantile wis |
