R: Neyman-Pearson Classification via Cost-Sensitive Learning

# Neyman-Pearson Classification via Cost-Sensitive Learning

## Documentation for package ‘npcs’ version 0.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

cv.npcs | Compare the performance of the NPMC-CX, NPMC-ER, and vanilla models through cross-validation or bootstrapping methods
error_rate | Calculate the error rates for each class.
gamma_smote | Gamma-synthetic minority over-sampling technique (gamma-SMOTE).
generate_data | Generate the data.
npcs | Fit a multi-class Neyman-Pearson classifier with error controls via cost-sensitive learning.
predict.npcs | Predict new labels from new data based on the fitted NPMC classifier.
print.cv.npcs | Print the cv.npcs object.