Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Alexander Jordan, Fabian Krüger, Sebastian Lerch
Title: Evaluating Probabilistic Forecasts with scoringRules
Abstract: Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature.

Page views:: 4119. Submitted: 2017-11-08. Published: 2019-08-21.
Paper: Evaluating Probabilistic Forecasts with scoringRules     Download PDF (Downloads: 1474)
scoringRules_1.0.0.tar.gz: R source package Download (Downloads: 74; 1MB)
v90i12.R: R replication code Download (Downloads: 112; 14KB)

DOI: 10.18637/jss.v090.i12

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.