wevid: Quantifying Performance of a Binary Classifier Through Weight of Evidence

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

Version:	0.6.2
Depends:	R (≥ 2.10)
Imports:	ggplot2, mclust, pROC (≥ 1.9), reshape2, zoo
Suggests:	testthat (≥ 2.0.0)
Published:	2019-09-12
DOI:	10.32614/CRAN.package.wevid
Author:	Paul McKeigue [aut], Marco Colombo [ctb, cre]
Maintainer:	Marco Colombo <mar.colombo13 at gmail.com>
License:	GPL-3
URL:	http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html
NeedsCompilation:	no
Citation:	wevid citation info
CRAN checks:	wevid results

Documentation:

Reference manual:

wevid.pdf

Downloads:

Package source:	wevid_0.6.2.tar.gz
Windows binaries:	r-devel: wevid_0.6.2.zip, r-release: wevid_0.6.2.zip, r-oldrel: wevid_0.6.2.zip
macOS binaries:	r-release (arm64): wevid_0.6.2.tgz, r-oldrel (arm64): wevid_0.6.2.tgz, r-release (x86_64): wevid_0.6.2.tgz, r-oldrel (x86_64): wevid_0.6.2.tgz
Old sources:	wevid archive

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