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
Roxygen: list(markdown = 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
Config/pak/sysreqs: libglpk-dev libxml2-dev
Repository: https://s-kganz.r-universe.dev
Date/Publication: 2023-11-18 18:34:36 UTC
RemoteUrl: https://github.com/s-kganz/scutr
RemoteRef: HEAD
RemoteSha: 624f415cd45406d862f335028b41cc153861d279
NeedsCompilation: no
Packaged: 2026-07-12 07:30:15 UTC; root
Author: Keenan Ganz [aut, cre]