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>.
| 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 |
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