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

# Quantifying Performance of a Binary Classifier Through Weight of Evidence

## Documentation for package ‘wevid’ version 0.6.2

- DESCRIPTION file

## Help Pages

wevid-package | Quantifying performance of a diagnostic test using the sampling distribution of the weight of evidence favouring case over noncase status
auroc.crude | Summary evaluation of predictive performance
auroc.model | Summary evaluation of predictive performance
cleveland | Example datasets
fitonly | Example datasets
lambda.crude | Summary evaluation of predictive performance
lambda.model | Summary evaluation of predictive performance
mean.Wdensities | Summary evaluation of predictive performance
pima | Example datasets
plotcumfreqs | Plot the cumulative frequency distributions in cases and in controls
plotroc | Plot crude and model-based ROC curves
plotWdists | Plot the distribution of the weight of evidence in cases and in controls
prop.belowthreshold | Proportions of cases and controls below a threshold of weight of evidence
recalibrate.p | Recalibrate posterior probabilities
summary-densities | Summary evaluation of predictive performance
summary.Wdensities | Summary evaluation of predictive performance
Wdensities | Compute densities of weights of evidence in cases and controls
weightsofevidence | Calculate weights of evidence in natural log units
wevid | Quantifying performance of a diagnostic test using the sampling distribution of the weight of evidence favouring case over noncase status
wevid.datasets | Example datasets