https://wobushics.r-universe.devRecent package updates in wobushicsR-universehttps://github.com/wobushics.pnghttps://wobushics.r-universe.devSun, 08 Feb 2026 16:40:07 GMTchshen3-c@my.cityu.edu.hk (Che Shen)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>.https://github.com/r-universe/wobushics/actions/runs/25789655258Sun, 08 Feb 2026 16:40:07 GMTHNPclassifier0.1.0successhttps://wobushics.r-universe.devhttps://github.com/cran/HNPclassifier