scoringutils: Utilities for Scoring and Assessing Predictions

Combines a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>) with an easy to use wrapper that can be used to automatically evaluate predictions. Apart from proper scoring rules functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of forecasts. Several types of predictions can be evaluated: probabilistic forecasts (generally predictive samples generated by Markov Chain Monte Carlo procedures), quantile forecasts or point forecasts. Observed values and predictions can be either continuous, integer, or binary. Users can either choose to apply these rules separately in a vector / matrix format that can be flexibly used within other packages, or they can choose to do an automatic evaluation of their forecasts. This is implemented with 'data.table' and provides a consistent and very efficient framework for evaluating various types of predictions.

Version:	0.1.0
Depends:	R (≥ 2.10)
Imports:	data.table, goftest, graphics, scoringRules, stats
Suggests:	testthat, knitr, rmarkdown
Published:	2020-06-14
Author:	Nikos Bosse [aut, cre], Sam Abbott [aut], Joel Hellewell [ctb], Sophie Meakins [ctb], James Munday [ctb], Katharine Sherratt [ctb], Sebastian Funk [aut]
Maintainer:	Nikos Bosse <nikosbosse at gmail.com>
BugReports:	https://github.com/epiforecasts/scoringutils/issues
License:	MIT + file LICENSE
URL:	https://github.com/epiforecasts/scoringutils
NeedsCompilation:	no
Materials:	README
In views:	TimeSeries
CRAN checks:	scoringutils results

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