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HNPclassifier  

Hierarchical Neyman-Pearson Classification for Ordered Classes
View on CRAN: Click here


Download and install HNPclassifier package within the R console
Install from CRAN:
install.packages("HNPclassifier")

Install from Github:
library("remotes")
install_github("cran/HNPclassifier")

Install by package version:
library("remotes")
install_version("HNPclassifier", "0.1.0")



Attach the package and use:
library("HNPclassifier")
Maintained by
Che Shen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-02-08
Latest Update: 2026-02-08
Description:
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>.
How to cite:
Che Shen (2026). HNPclassifier: Hierarchical Neyman-Pearson Classification for Ordered Classes. R package version 0.1.0, https://cran.r-project.org/web/packages/HNPclassifier. Accessed 26 Jun. 2026.
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