Package: scutr
Title: Balancing Multiclass Datasets for Classification Tasks
Version: 0.2.0
Authors@R: 
    person(given = "Keenan",
           family = "Ganz",
           role = c("aut", "cre"),
           email = "ganzkeenan1@gmail.com")
Maintainer: Keenan Ganz <ganzkeenan1@gmail.com>
Description: Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Imports: smotefamily, parallel, mclust
Depends: R (>= 2.10)
URL: https://github.com/s-kganz/scutr
BugReports: https://github.com/s-kganz/scutr/issues
Suggests: testthat (>= 2.0.0)
Config/testthat/edition: 2
NeedsCompilation: no
Packaged: 2023-11-17 22:42:02 UTC; rsgal
Author: Keenan Ganz [aut, cre]
Repository: CRAN
Date/Publication: 2023-11-17 23:10:02 UTC
Built: R 4.6.0; ; 2026-04-26 04:07:21 UTC; unix
