HNPclassifier
Hierarchical Neyman-Pearson Classification for Ordered Classes
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling source/ R- | HNPclassifier_0.1.0.tar.gz |
15.4 KiB |
0.1.0 |
rolling linux/jammy R-4.5 | HNPclassifier_0.1.0.tar.gz |
74.3 KiB |
0.1.0 |
rolling linux/noble R-4.5 | HNPclassifier_0.1.0.tar.gz |
74.0 KiB |
0.1.0 |
latest source/ R- | HNPclassifier_0.1.0.tar.gz |
15.4 KiB |
0.1.0 |
latest linux/jammy R-4.5 | HNPclassifier_0.1.0.tar.gz |
74.3 KiB |
0.1.0 |
latest linux/noble R-4.5 | HNPclassifier_0.1.0.tar.gz |
74.0 KiB |
0.1.0 |
2026-04-23 source/ R- | HNPclassifier_0.1.0.tar.gz |
15.4 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | HNPclassifier_0.1.0.zip |
77.1 KiB |