HNPclassifier

Description

The Hierarchical Neyman-Pearson (H-NP) classification framework extends the Neyman-Pearson classification paradigm to multi-class settings where classes have a natural priority ordering. This is particularly useful for classification in unbalanced dataset, for example, disease severity classification, where under-classification errors (misclassifying patients into less severe categories) are more consequential than other misclassifications. The package implements H-NP umbrella algorithms that controls under-classification errors under user specified control levels with high probability. It supports the creation of H-NP classifiers using scoring functions based on built-in classification methods (including logistic regression, support vector machines, and random forests), as well as user-trained scoring functions.

Downloads

Trend: +49.2% (30d vs prior 30d)

CRAN Check Status

Check History

Dependency Network

R Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Feb 8, 2026. Releases before tracking aren’t shown.