HNPclassifier
0.2.1Hierarchical Neyman-Pearson Classification for Ordered Classes
Overview
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.
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Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 43%
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Code & Tests
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- 0.2.1Latest
- 0.2.02026-06-27 · diff ↗
- RR 4.6.0 released · 2026-04-24
- 0.1.02026-03-10
- RR 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
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