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.

翻译：在多类分类问题中，各类别通常存在自然的优先级顺序（例如癌症分期、COVID-19严重程度等级或空气质量类别）。在此类情境下，优先确保对更严重类别的正确识别，并控制低估分类错误（即高优先级类别的观测值被误判为低优先级类别）至关重要。Wang等人（2024）提出的分层奈曼-皮尔逊（H-NP）框架专为有序多类场景设计，旨在优先控制低估分类错误；其H-NP伞形算法能以用户指定水平实现高概率的低估分类错误控制。本文介绍R语言包HNPclassifier，该包通过内置学习器（如逻辑回归、随机森林和支持向量机）及用户提供的评分函数实现H-NP伞形算法，从而构建H-NP分类器，为有序多类分类任务提供有效的错误控制。