Hierarchical Neyman-Pearson Classification for Ordered Classes R package HNPclassifier version 0.1.0 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. For theoretical details, please refer to Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong (2024) <doi:10.1080/01621459.2023.2270657>. Software Imports: dplyr, e1071, nnet, randomForest Che Shen <chshen3-c@my.cityu.edu.hk> Comprehensive R Archive Network (CRAN) Che Shen [aut, cre] (Implementation and maintenance), Lujia Yang [aut] (Testing and debugging), Lijia Wang [aut] (Original theory and supervision), Shunan Yao [aut] (Supervision and debugging) MIT + file LICENSE (https://CRAN.R-project.org/package=HNPclassifier/LICENSE) 2026-02-08 application/tgz https://CRAN.R-project.org/package=HNPclassifier doi:10.32614/CRAN.package.HNPclassifier