infer_rel_skill_metric

Helper function to infer the metric for which pairwise comparisons shall
be made. The function simply checks the names of the available columns and
chooses the most widely used metric.

internal

Provides a collection of metrics and proper scoring rules
(Tilmann Gneiting & Adrian E Raftery (2007)
<doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019)
<doi:10.18637/jss.v090.i12>) within a consistent framework for
evaluation, comparison and visualisation of forecasts.
In addition to 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 (e.g. binary, discrete, continuous) which may
come in different formats (e.g. forecasts represented by predictive samples
or by quantiles of the predictive distribution) can be evaluated.
Scoring metrics can be used either through a convenient data.frame format,
or can be applied as individual functions in a vector / matrix format.
All functionality has been implemented with a focus on performance and is
robustly tested. Find more information about the package in the
accompanying paper (<doi:10.48550/arXiv.2205.07090>).

Nikos Bosse

scoringutils

Utilities for Scoring and Assessing Predictions

Sam Abbott 

Hugo Gruson

Johannes Bracher 

Sebastian Funk

infer_rel_skill_metric function

<dl><dt>scores</dt>
<dd>A data.table of scores as produced by <code>score()</code>.</dd></dl>

Arguments

Infer metric for pairwise comparisons — infer_rel_skill_metric

<dl>

<dt>scores</dt>
<dd>A data.table of scores as produced by <code>score()</code>.</dd>

</dl>

infer_rel_skill_metric: Infer metric for pairwise comparisons

Description

Usage

Arguments