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 (2018), <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
Depends:	R (≥ 2.10)
Imports:	ggplot2, mclust, pROC, reshape2, zoo
Published:	2019-04-04
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

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