print.scoringutils_check

Helper function that prints the output generated by
<code>check_forecasts()</code>

check-forecasts

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

print.scoringutils_check function

<dl><dt>x</dt>
<dd>An object of class 'scoringutils_check' as produced by
<code>check_forecasts()</code></dd>
<dt>...</dt>
<dd>additional arguments (not used here)</dd></dl>

Arguments

Print output from check_forecasts() — print.scoringutils_check

<dl>

<dt>x</dt>
<dd>An object of class 'scoringutils_check' as produced by
<code>check_forecasts()</code></dd>


<dt>...</dt>
<dd>additional arguments (not used here)</dd>

</dl>

print.scoringutils_check: Print output from `check_forecasts()`

Description

Usage

Arguments

Examples