# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "HNPclassifier" in publications use:' type: software license: MIT title: 'HNPclassifier: Hierarchical Neyman-Pearson Classification for Ordered Classes' version: 0.1.0 doi: 10.32614/CRAN.package.HNPclassifier abstract: 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) . authors: - family-names: Shen given-names: Che email: chshen3-c@my.cityu.edu.hk - family-names: Yang given-names: Lujia email: 25480847@life.hkbu.edu.hk - family-names: Wang given-names: Lijia email: lijiwang@cityu.edu.hk - family-names: Yao given-names: Shunan email: yaoshunan@hkbu.edu.hk repository: https://wobushics.r-universe.dev commit: 98969310356c56b40ee3bbbd1c4cb562a3217815 date-released: '2026-02-08' contact: - family-names: Shen given-names: Che email: chshen3-c@my.cityu.edu.hk