Package index
-
scoringutils
scoringutils-package
- scoringutils: Utilities for Scoring and Assessing Predictions
-
example_binary
- Binary forecast example data
-
example_nominal
- Nominal 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
-
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_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
-
set_forecast_unit()
- Set unit of a single forecast manually
-
assert_forecast()
- Assert that input is a forecast object and passes validations
-
is_forecast_binary()
is_forecast_nominal()
is_forecast_point()
is_forecast_quantile()
is_forecast_sample()
is_forecast()
- Test whether an object is a forecast object
-
get_forecast_counts()
- Count number of available forecasts
-
print(<forecast>)
- Print information about a forecast object
-
get_duplicate_forecasts()
- Find duplicate forecasts
-
get_forecast_unit()
- Get unit of a single forecast
-
as_forecast_point()
- Create a
forecast
object for point forecasts
-
as_forecast_quantile()
- Create a
forecast
object for quantile-based forecasts
-
log_shift()
- Log transformation with an additive shift
-
transform_forecasts()
- Transform forecasts and observed values
-
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_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
-
select_metrics()
- Select metrics from a list of functions
-
add_relative_skill()
- Add relative skill scores based on pairwise comparisons
-
get_correlations()
- Calculate correlation between metrics
-
get_coverage()
- Get quantile and interval coverage values for quantile-based forecasts
-
get_pairwise_comparisons()
- Obtain pairwise comparisons between models
-
get_pit_histogram()
- Probability integral transformation histogram
-
score()
- Evaluate forecasts
-
summarise_scores()
summarize_scores()
- Summarise scores as produced by
score()
-
ae_median_quantile()
- Absolute error of the median (quantile-based version)
-
ae_median_sample()
- Absolute error of the median (sample-based version)
-
bias_quantile()
- Determines bias of quantile forecasts
-
bias_sample()
- Determine bias of forecasts
-
crps_sample()
dispersion_sample()
overprediction_sample()
underprediction_sample()
- (Continuous) ranked probability score
-
dss_sample()
- Dawid-Sebastiani score
-
interval_coverage()
- Interval coverage (for quantile-based forecasts)
-
interval_score()
- Interval score
-
logs_sample()
- Logarithmic score (sample-based version)
-
mad_sample()
- Determine dispersion of a probabilistic forecast
-
pit_histogram_sample()
- Probability integral transformation for counts
-
quantile_score()
- Quantile score
-
brier_score()
logs_binary()
- Metrics for binary outcomes
-
logs_nominal()
- Log score for nominal outcomes
-
se_mean_sample()
- Squared error of the mean (sample-based version)
-
wis()
dispersion_quantile()
overprediction_quantile()
underprediction_quantile()
- Weighted interval score (WIS)
-
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
-
theme_scoringutils()
- Scoringutils ggplot2 theme
-
assert_dims_ok_point()
- Assert Inputs Have Matching Dimensions
-
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_nominal()
- Assert that inputs are correct for nominal 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
-
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
-
get_forecast_type()
- Get forecast type from forecast object
-
get_type()
- Get type of a vector or matrix of observed values or predictions
-
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
-
validate_metrics()
- Validate metrics
-
apply_metrics()
- Apply a list of functions to a data table of forecasts
-
as_scores()
- Create an object of class
scores
from data
-
assert_scores()
- Validate an object of class
scores
-
bias_quantile_single_vector()
- Compute bias for a single vector of quantile predictions
-
clean_forecast()
- Clean forecast object
-
compare_forecasts()
- Compare a subset of common forecasts
-
document_assert_functions
- Documentation template for assert functions
-
document_check_functions
- Documentation template for check functions
-
document_test_functions
- Documentation template for test functions
-
ensure_data.table()
- Ensure that an object is a
data.table
-
forecast_types
- Documentation template for forecast types
-
geometric_mean()
- Calculate geometric mean
-
get_protected_columns()
- Get protected columns from data
-
get_range_from_quantile()
- Get interval range belonging to a quantile
-
illustration-input-metric-binary-point
- Illustration of required inputs for binary and point forecasts
-
illustration-input-metric-nominal
- Illustration of required inputs for nominal 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 not available
-
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
-
quantile_to_interval()
quantile_to_interval_dataframe()
quantile_to_interval_numeric()
- 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 range format
-
scoringutils
scoringutils-package
- scoringutils: Utilities for Scoring and Assessing Predictions