Utilities for Scoring and Assessing Predictions

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Documentation for package ‘scoringutils’ version 0.1.4

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ae_median Absolute Error of the Median
bias Determines bias of forecasts
binary_example_data Binary Example Data
brier_score Brier Score
continuous_example_data Continuous Example Data
correlation_plot Plot Correlation Between Metrics
crps Ranked Probability Score
dss Dawid-Sebastiani Score
eval_forecasts Evaluate forecasts
hist_PIT PIT Histogram
hist_PIT_quantile PIT Histogram Quantile
integer_example_data Integer Example Data
interval_coverage Plot Interval Coverage
interval_score Interval Score
logs LogS
mse Mean Squared Error
pit Probability Integral Transformation
plot_predictions Plot Predictions vs True Values
quantile_bias Determines Bias of Quantile Forecasts
quantile_coverage Plot Quantile Coverage
quantile_example_data_long Quantile Example Data - Long Format
quantile_example_data_plain Quantile Example Data - Plain Quantile Format
quantile_example_data_wide Quantile Example Data - Wide Format
quantile_to_long Pivot Quantile Forecasts From Wide to Long Format
quantile_to_range Change Data from a Plain Quantile Format to a Range Format
quantile_to_wide Pivot Quantile Forecasts From Long to Wide Format
range_plot Plot Metrics by Range of the Prediction Interval
range_to_quantile Change Data from a Range Format to a Plain Quantile Format
sample_to_quantile Change Data from a Sample Based Format to a Quantile Format
sample_to_range Change Data from a Sample Based Format to a Interval Range Format
score_heatmap Create a Heatmap of a Scoring Metric
score_table Plot Coloured Score Table
scoringutils scoringutils
sharpness Determines sharpness of a probabilistic forecast
wis_components Plot Contributions to the Weighted Interval Score