epiforecasts/scoringutils: 1.0.0

Major update to the package and most package functions with lots of breaking changes.

Feature updates

• new and updated Readme and vignette
• the proposed scoring workflow was reworked. Functions were changed so they can easily be piped and have simplified arguments and outputs.

new functions and function changes

• the function eval_forecasts() was replaced by a function score() with a much reduced set of function arguments.
• Functionality to summarise scores and to add relative skill scores was moved to a function summarise_scores()
• new function check_forecasts() to analyse input data before scoring
• new function correlation() to compute correlations between different metrics
• new function add_coverage() to add coverage for specific central prediction intervals
• new function avail_forecasts() allows to visualise the number of available forecasts
• new function find_duplicates() to find duplicate forecasts which cause an error
• all plotting functions were renamed to begin with plot_. Arguments were simplified
• the function pit() now works based on data.frames. The old pit function was renamed to pit_sample(). PIT p-values were removed entirely.
• the function plot_pit() now works directly with input as produced by pit()
• many data-handling functions were removed and input types for score() were restricted to sample-based, quantile-based or binary forecasts.
• the function brier_score() now returns all brier scores, rather than taking the mean before returning an output.
• crps, dss and logs were renamed to crps_sample(), dss_sample(), and logs_sample()

Bug fixes

• Testing was expanded
• minor bugs were fixed, for example a bug in the sample_to_quantile function (https://github.com/epiforecasts/scoringutils/pull/223)

package data updated

• package data is now based on forecasts submitted to the European Forecast Hub (https://covid19forecasthub.eu/).
• all example data files were renamed to begin with example_
• a new data set, summary_metrics was included that contains a summary of the metrics implemented in scoringutils

Other breaking changes

• The 'sharpness' component of the weighted interval score was renamed to dispersion. This was done to make it more clear what the component represents and to maintain consistency with what is used in other places.