Man pages for epiforecasts/scoringutils
Utilities for Scoring and Assessing Predictions
| add_relative_skill | Add relative skill scores based on pairwise comparisons |
| ae_median_quantile | Absolute error of the median (quantile-based version) |
| ae_median_sample | Absolute error of the median (sample-based version) |
| apply_metrics | Apply a list of functions to a data table of forecasts |
| as_forecast | Create a 'forecast' object |
| as_scores | Create an object of class 'scores' from data |
| assert_dims_ok_point | Assert Inputs Have Matching Dimensions |
| assert_forecast | Assert that input is a forecast object and passes validations |
| assert_forecast_generic | Validation common to all forecast types |
| assert_forecast_type | Assert that forecast type is as expected |
| assert_input_binary | Assert that inputs are correct for binary forecast |
| assert_input_interval | Assert that inputs are correct for interval-based forecast |
| assert_input_point | Assert that inputs are correct for point forecast |
| assert_input_quantile | Assert that inputs are correct for quantile-based forecast |
| assert_input_sample | Assert that inputs are correct for sample-based forecast |
| bias_quantile | Determines bias of quantile forecasts |
| bias_quantile_single_vector | Compute bias for a single vector of quantile predictions |
| bias_sample | Determine bias of forecasts |
| check_columns_present | Check column names are present in a data.frame |
| check_dims_ok_point | Check Inputs Have Matching Dimensions |
| check_duplicates | Check that there are no duplicate forecasts |
| check_input_binary | Check that inputs are correct for binary forecast |
| check_input_interval | Check that inputs are correct for interval-based forecast |
| check_input_point | Check that inputs are correct for point forecast |
| check_input_quantile | Check that inputs are correct for quantile-based forecast |
| check_input_sample | Check that inputs are correct for sample-based forecast |
| check_number_per_forecast | Check that all forecasts have the same number of quantiles or... |
| check_numeric_vector | Check whether an input is an atomic vector of mode 'numeric' |
| check_try | Helper function to convert assert statements into checks |
| clean_forecast | Clean forecast object |
| compare_two_models | Compare two models based on subset of common forecasts |
| crps_sample | (Continuous) ranked probability score |
| customise_metric | Customises a metric function with additional arguments. |
| document_assert_functions | Documentation template for assert functions |
| document_check_functions | Documentation template for check functions |
| document_test_functions | Documentation template for test functions |
| dss_sample | Dawid-Sebastiani score |
| ensure_data.table | Ensure that an object is a 'data.table' |
| ensure_model_column | Assure that data has a 'model' column |
| example_binary | Binary forecast example data |
| example_point | Point forecast example data |
| example_quantile | Quantile example data |
| example_sample_continuous | Continuous forecast example data |
| example_sample_discrete | Discrete forecast example data |
| forecast_types | Documentation template for forecast types |
| geometric_mean | Calculate geometric mean |
| get_correlations | Calculate correlation between metrics |
| get_coverage | Get quantile and interval coverage values for quantile-based... |
| get_duplicate_forecasts | Find duplicate forecasts |
| get_forecast_counts | Count number of available forecasts |
| get_forecast_type | Infer forecast type from data |
| get_forecast_unit | Get unit of a single forecast |
| get_metrics | Get names of the metrics that were used for scoring |
| get_pairwise_comparisons | Obtain pairwise comparisons between models |
| get_pit | Probability integral transformation (data.frame version) |
| get_protected_columns | Get protected columns from data |
| get_range_from_quantile | Get interval range belonging to a quantile |
| get_type | Get type of a vector or matrix of observed values or... |
| interpolate_median | Helper function to interpolate the median prediction if it is... |
| interval_coverage | Interval coverage (for quantile-based forecasts) |
| interval_coverage_deviation | Interval coverage deviation (for quantile-based forecasts) |
| interval_long_to_quantile | Change forecast from an interval format to a quantile format |
| interval_score | Interval score |
| is_forecast | Test whether an object is a forecast object |
| log_shift | Log transformation with an additive shift |
| logs_sample | Logarithmic score (sample-based version) |
| mad_sample | Determine dispersion of a probabilistic forecast |
| metrics_binary | Default metrics and scoring rules for binary forecasts |
| metrics_point | Default metrics and scoring rules for point forecasts |
| metrics_quantile | Default metrics and scoring rules for quantile-based... |
| metrics_sample | Default metrics and scoring rules sample-based forecasts |
| new_forecast | Class constructor for 'forecast' objects |
| new_scores | Construct an object of class 'scores' |
| pairwise_comparison_one_group | Do pairwise comparison for one set of forecasts |
| permutation_test | Simple permutation test |
| pit_sample | Probability integral transformation (sample-based version) |
| plot_correlations | Plot correlation between metrics |
| plot_forecast_counts | Visualise the number of available forecasts |
| plot_heatmap | Create a heatmap of a scoring metric |
| plot_interval_coverage | Plot interval coverage |
| plot_pairwise_comparisons | Plot heatmap of pairwise comparisons |
| plot_pit | PIT histogram |
| plot_quantile_coverage | Plot quantile coverage |
| plot_wis | Plot contributions to the weighted interval score |
| print.forecast_binary | Print information about a forecast object |
| quantile_score | Quantile score |
| quantile_to_interval | Transform from a quantile format to an interval format |
| run_safely | Run a function safely |
| sample_to_interval_long | Change data from a sample-based format to a long interval... |
| sample_to_quantile | Change data from a sample based format to a quantile format |
| score | Evaluate forecasts |
| scoring-functions-binary | Metrics for binary outcomes |
| scoringutils-package | scoringutils: Utilities for Scoring and Assessing Predictions |
| select_metrics | Select metrics from a list of functions |
| se_mean_sample | Squared error of the mean (sample-based version) |
| set_forecast_unit | Set unit of a single forecast manually |
| summarise_scores | Summarise scores as produced by 'score()' |
| test_columns_not_present | Test whether column names are NOT present in a data.frame |
| test_columns_present | Test whether all column names are present in a data.frame |
| test_forecast_type_is_binary | Test whether data could be a binary forecast. |
| test_forecast_type_is_point | Test whether data could be a point forecast. |
| test_forecast_type_is_quantile | Test whether data could be a quantile forecast. |
| test_forecast_type_is_sample | Test whether data could be a sample-based forecast. |
| theme_scoringutils | Scoringutils ggplot2 theme |
| transform_forecasts | Transform forecasts and observed values |
| validate_forecast | Re-validate an existing forecast object |
| validate_metrics | Validate metrics |
| validate_scores | Validate an object of class 'scores' |
| wis | Weighted interval score (WIS) |
