Package: HNPclassifier
Title: Hierarchical Neyman-Pearson Classification for Ordered Classes
Version: 0.1.0
Authors@R: 
       c(person(given = "Che", family = "Shen", role = c("aut", "cre"), 
             email = "chshen3-c@my.cityu.edu.hk", comment = "Implementation and maintenance"),
      person(given = "Lujia", family = "Yang", role = "aut", 
             email = "25480847@life.hkbu.edu.hk", comment = "Testing and debugging"),
      person(given = "Lijia", family = "Wang", role = "aut", 
             email = "lijiwang@cityu.edu.hk", comment = "Original theory and supervision"),
      person(given = "Shunan", family = "Yao", role = "aut", 
             email = "yaoshunan@hkbu.edu.hk", comment = "Supervision and debugging"))
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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: dplyr, e1071, nnet, randomForest
NeedsCompilation: no
Packaged: 2026-02-04 18:16:28 UTC; scsma
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@my.cityu.edu.hk>
Repository: CRAN
Date/Publication: 2026-02-08 16:40:07 UTC
Built: R 4.6.0; ; 2026-04-29 03:40:46 UTC; unix
