# Utilities for Scoring and Assessing Predictions

## Documentation for package ‘scoringutils’ version 2.1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E G I L M O P Q R S T U V W

## -- A --

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)
assert_dims_ok_point | Assert Inputs Have Matching Dimensions
assert_forecast | Assert that input is a forecast object and passes validations
assert_forecast.default | Assert that input is a forecast object and passes validations
assert_forecast.forecast_binary | Assert that input is a forecast object and passes validations
assert_forecast.forecast_point | Assert that input is a forecast object and passes validations
assert_forecast.forecast_quantile | Assert that input is a forecast object and passes validations
assert_forecast.forecast_sample | 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
as_forecast_binary | Create a 'forecast' object for binary forecasts
as_forecast_binary.default | 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_nominal.default | Create a 'forecast' object for nominal forecasts
as_forecast_ordinal | Create a 'forecast' object for ordinal forecasts
as_forecast_ordinal.default | Create a 'forecast' object for ordinal forecasts
as_forecast_point | Create a 'forecast' object for point forecasts
as_forecast_point.default | Create a 'forecast' object for point forecasts
as_forecast_point.forecast_quantile | Create a 'forecast' object for point forecasts
as_forecast_quantile | Create a 'forecast' object for quantile-based forecasts
as_forecast_quantile.default | Create a 'forecast' object for quantile-based forecasts
as_forecast_quantile.forecast_sample | Create a 'forecast' object for quantile-based forecasts
as_forecast_sample | Create a 'forecast' object for sample-based forecasts
as_forecast_sample.default | Create a 'forecast' object for sample-based forecasts

## -- B --

bias_quantile | Determines bias of quantile forecasts
bias_sample | Determine bias of forecasts
brier_score | Metrics for binary outcomes

## -- C --

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
crps_sample | (Continuous) ranked probability score

## -- D --

dispersion_quantile | Weighted interval score (WIS)
dispersion_sample | (Continuous) ranked probability score
dss_sample | Dawid-Sebastiani score

## -- E --

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

## -- G --

get_correlations | Calculate correlation between metrics
get_coverage | Get quantile and interval coverage values for quantile-based forecasts
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_pit_histogram.default | Probability integral transformation histogram
get_pit_histogram.forecast_quantile | Probability integral transformation histogram
get_pit_histogram.forecast_sample | Probability integral transformation histogram
get_type | Get type of a vector or matrix of observed values or predictions

## -- I --

interval_coverage | Interval coverage (for quantile-based forecasts)
interval_score | Interval score
is_forecast | Test whether an object is a forecast object
is_forecast_binary | Test whether an object is a forecast object
is_forecast_nominal | Test whether an object is a forecast object
is_forecast_ordinal | Test whether an object is a forecast object
is_forecast_point | Test whether an object is a forecast object
is_forecast_quantile | Test whether an object is a forecast object
is_forecast_sample | Test whether an object is a forecast object

## -- L --

logs_binary | Metrics for binary outcomes
logs_categorical | Log score for categorical outcomes
logs_sample | Logarithmic score (sample-based version)
log_shift | Log transformation with an additive shift

## -- M --

mad_sample | Determine dispersion of a probabilistic forecast

## -- O --

overprediction_quantile | Weighted interval score (WIS)
overprediction_sample | (Continuous) ranked probability score

## -- P --

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

## -- Q --

quantile_score | Quantile score

## -- R --

rps_ordinal | Ranked Probability Score for ordinal outcomes

## -- S --

score | Evaluate forecasts
score.forecast_binary | Evaluate forecasts
score.forecast_nominal | Evaluate forecasts
score.forecast_ordinal | Evaluate forecasts
score.forecast_point | Evaluate forecasts
score.forecast_quantile | Evaluate forecasts
score.forecast_sample | Evaluate forecasts
scoring-functions-binary | Metrics for binary outcomes
select_metrics | Select metrics from a list of functions
set_forecast_unit | Set unit of a single forecast manually
se_mean_sample | Squared error of the mean (sample-based version)
summarise_scores | Summarise scores as produced by 'score()'
summarize_scores | Summarise scores as produced by 'score()'

## -- T --

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

## -- U --

underprediction_quantile | Weighted interval score (WIS)
underprediction_sample | (Continuous) ranked probability score

## -- V --

validate_metrics | Validate metrics

## -- W --

wis | Weighted interval score (WIS)