HNPclassifier: An R Package for Hierarchical Neyman-Pearson Classification

Lujia Yang; Che Shen; Shunan Yao; Lijia Wang

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arXiv · 2606.13523

HNPclassifier: An R Package for Hierarchical Neyman-Pearson Classification

Lujia Yang·Che Shen·Shunan Yao·Lijia Wang

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Abstract

In multi-class classification problems, classes often have a natural priority ordering (e.g., cancer stages, COVID-19 severity levels, or air-quality categories). In such settings, it is important to prioritize correct identification of more severe classes and to control under-classification errors, which occur when an observation from a higher-priority class is misclassified into a lower-priority one. The Hierarchical Neyman-Pearson (H-NP) framework of Wang et al. (2024) was developed for ordered multi-class settings to prioritize under-classification error control; its H-NP umbrella algorithm provides high-probability control of under-classification errors at user-specified levels. This paper introduces the R package HNPclassifier, which implements H-NP umbrella algorithms to construct H-NP classifiers using built-in learners such as logistic regression, random forests, and support vector machines, as well as user-supplied scoring functions, thereby enabling effective error control for ordered multi-class classification tasks.

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Lujia Yang, Che Shen, Shunan Yao, Lijia Wang. 2026-06-11. HNPclassifier: An R Package for Hierarchical Neyman-Pearson Classification. https://arxiv.org/abs/2606.13523

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