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wevid
View on CRAN: Click
here
Download and install wevid package within the R console
Install from CRAN:
install.packages("wevid")
Install from Github:
library("remotes")
install_github("cran/wevid")
Install by package version:
library("remotes")
install_version("wevid", "0.6.2")
Attach the package and use:
library("wevid")
Maintained by
Marco Colombo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-06-15
Latest Update: 2019-09-12
Description:
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.
How to cite:
Marco Colombo (2018). wevid: Quantifying Performance of a Binary Classifier Through Weight of Evidence. R package version 0.6.2, https://cran.r-project.org/web/packages/wevid
Previous versions and publish date:
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Other R packages that wevid depends,
imports, suggests or enhances
Functions, R codes and Examples using
the wevid R package
Some associated functions: Wdensities.crude . Wdensities . plotWdists . plotcumfreqs . plotroc . prop.belowthreshold . recalibrate.p . summary-densities . weightsofevidence . wevid-package . wevid.datasets .
Some associated R codes: data.R . plots.R . wdists.R . wevid.R . Full wevid package functions and examples
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