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HNPclassifier

0.2.1

Hierarchical Neyman-Pearson Classification for Ordered Classes

0packages depend
1.3Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Che ShenFirst published 2026-02-083 releasesCRAN page ↗

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. The package exports `base_function()` to train these built-in base learners directly for use in the H-NP pipeline.

Install

Health

CRAN checks
13OK
Slowest check: 1.3 min · r-oldrel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
5
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesNopkgdown siteNoNEWSYes · 67% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
43%

Downloads

1.3K
CRAN downloads in the past year
Rank #17,243 · ~4/day · ~111/mo
Daily download trend is not available in this view yet.
16330 days
63990 days
1.3K1 year
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Also on45 r2u21 autocran

Dependencies

Declared dependencies
3 external dependencies (excludes base and recommended)
Depends (0)
none
Imports (5)
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (4)
Maintainer (1)
Author, Maintainer
Authors (4)
Author, Maintainer
Package Timeline

3 releases. Pick two to compare their code metrics. R releases are shown for context.

  • 0.2.1Latest
    2026-07-14 · current release · diff ↗
  • 0.2.0
    2026-06-27 · diff ↗
  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.0
    2026-03-10
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2026-02-08
Total releases
3 / 1 yrs
License
MIT + file LICENSE OSI
Download size
26 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("HNPclassifier")
Shen, C., Wang, L., Yang, L., & Yao, S. (2026). HNPclassifier: Hierarchical Neyman-Pearson Classification for Ordered Classes (Version 0.2.1) [Computer software]. https://doi.org/10.32614/CRAN.package.HNPclassifier

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for HNPclassifier version 0.2.1 [Data set]. HJJB, LLC. Data release v2026-08-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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