Man pages for 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_binary	Create a 'forecast' object for binary forecasts
as_forecast_doc_template	General information on creating a 'forecast' object
as_forecast_generic	Common functionality for as_forecast_<type> functions
as_forecast_nominal	Create a 'forecast' object for nominal forecasts
as_forecast_ordinal	Create a 'forecast' object for ordinal forecasts
as_forecast_point	Create a 'forecast' object for point forecasts
as_forecast_quantile	Create a 'forecast' object for quantile-based forecasts
as_forecast_sample	Create a 'forecast' object for sample-based forecasts
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_categorical	Assert that inputs are correct for categorical forecasts
assert_input_interval	Assert that inputs are correct for interval-based forecast
assert_input_nominal	Assert that inputs are correct for nominal forecasts
assert_input_ordinal	Assert that inputs are correct for ordinal forecasts
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
assert_scores	Validate an object of class 'scores'
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 rows
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_forecasts	Compare a subset of common forecasts
crps_sample	(Continuous) ranked probability score
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'
example_binary	Binary forecast example data
example_nominal	Nominal example data
example_ordinal	Ordinal 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	Get forecast type from forecast object
get_forecast_unit	Get unit of a single forecast
get_metrics	Get metrics
get_metrics.forecast_binary	Get default metrics for binary forecasts
get_metrics.forecast_nominal	Get default metrics for nominal forecasts
get_metrics.forecast_ordinal	Get default metrics for nominal forecasts
get_metrics.forecast_point	Get default metrics for point forecasts
get_metrics.forecast_quantile	Get default metrics for quantile-based forecasts
get_metrics.forecast_sample	Get default metrics for sample-based forecasts
get_metrics.scores	Get names of the metrics that were used for scoring
get_pairwise_comparisons	Obtain pairwise comparisons between models
get_pit_histogram	Probability integral transformation histogram
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...
illustration-input-metric-binary-point	Illustration of required inputs for binary and point...
illustration-input-metric-nominal	Illustration of required inputs for nominal forecasts
illustration-input-metric-ordinal	Illustration of required inputs for ordinal forecasts
illustration-input-metric-quantile	Illustration of required inputs for quantile-based forecasts
illustration-input-metric-sample	Illustration of required inputs for sample-based forecasts
interpolate_median	Helper function to interpolate the median prediction if it is...
interval_coverage	Interval coverage (for quantile-based forecasts)
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
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_histogram_sample	Probability integral transformation for counts
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_quantile_coverage	Plot quantile coverage
plot_wis	Plot contributions to the weighted interval score
print.forecast	Print information about a forecast object
quantile_score	Quantile score
quantile_to_interval	Transform from a quantile format to an interval format
rps_ordinal	Ranked Probability Score for ordinal outcomes
run_safely	Run a function safely
sample_to_interval_long	Change data from a sample-based format to a long interval...
score	Evaluate forecasts
scoring-functions-binary	Metrics for binary outcomes
scoring-functions-nominal	Log score for categorical 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
theme_scoringutils	Scoringutils ggplot2 theme
transform_forecasts	Transform forecasts and observed values
validate_metrics	Validate metrics
wis	Weighted interval score (WIS)