wevid (0.6.1)

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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), ). 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.

Maintainer: Marco Colombo
Author(s): Paul McKeigue [aut] (<https://orcid.org/0000-0002-5217-1034>), Marco Colombo [ctb, cre] (<https://orcid.org/0000-0001-6672-0623>)

License: GPL-3

Uses: ggplot2, mclust, pROC, reshape2, zoo

Released about 1 month ago.

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