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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>.
| Version: | 0.1.0 |
| Imports: | dplyr, e1071, nnet, randomForest |
| Published: | 2026-02-08 |
| DOI: | 10.32614/CRAN.package.HNPclassifier (may not be active yet) |
| Author: | 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) |
| Maintainer: | Che Shen <chshen3-c at my.cityu.edu.hk> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | HNPclassifier results |
| Reference manual: | HNPclassifier.html , HNPclassifier.pdf |
| Package source: | HNPclassifier_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): HNPclassifier_0.1.0.tgz, r-oldrel (arm64): HNPclassifier_0.1.0.tgz, r-release (x86_64): HNPclassifier_0.1.0.tgz, r-oldrel (x86_64): HNPclassifier_0.1.0.tgz |
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