The scoringutils package provides a collection of metrics and proper scoring rules and aims to make it simple to score probabilistic forecasts against the true observed values. The scoringutils package offers convenient automated forecast evaluation in a data.table format (using the function score()), but also provides experienced users with a set of reliable lower-level scoring metrics operating on vectors/matriced they can build upon in other applications. In addition it implements a wide range of flexible plots designed to cover many use cases. Where available scoringutils depends on functionality from scoringRules which provides a comprehensive collection of proper scoring rules for predictive probability distributions represented as sample or parametric distributions. For some forecast types, such as quantile forecasts, scoringutils also implements additional metrics for evaluating forecasts. On top of providing an interface to the proper scoring rules implemented in scoringRules and natively, scoringutils also offers utilities for summarising and visualising forecasts and scores, and to obtain relative scores between models which may be useful for non-overlapping forecasts and forecasts across scales. Predictions can be handled in various formats: scoringutils can handle probabilistic forecasts in either a sample based or a quantile based format. For more detail on the expected input formats please see below. True values can be integer, continuous or binary, and appropriate scores for each of these value types are selected automatically.

R package