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ROBOSRMSMOTE: Robust Oversampling with RM-SMOTE for Imbalanced Classification

Provides the ROBOSRMSMOTE (Robust Oversampling with RM-SMOTE) framework for imbalanced classification tasks. This package extends Mahalanobis distance-based oversampling techniques by integrating robust covariance estimators to better handle outliers and complex data distributions. The implemented methodology builds upon and significantly expands the RM-SMOTE algorithm originally proposed by Taban et al. (2025) <doi:10.1007/s10260-025-00819-8>.

Version: 1.0.0
Depends: R (≥ 4.0.0)
Imports: rrcov (≥ 1.7.0), meanShiftR (≥ 0.56), stats
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2026-03-09
DOI: 10.32614/CRAN.package.ROBOSRMSMOTE (may not be active yet)
Author: Emre Dunder [aut], Mehmet Ali Cengiz [aut], Zainab Subhi Mahmood Hawrami [aut, cre], Abdulmohsen Alharthi [aut]
Maintainer: Zainab Subhi Mahmood Hawrami <zaianbsubhi at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: ROBOSRMSMOTE results

Documentation:

Reference manual: ROBOSRMSMOTE.html , ROBOSRMSMOTE.pdf

Downloads:

Package source: ROBOSRMSMOTE_1.0.0.tar.gz
Windows binaries: r-devel: ROBOSRMSMOTE_1.0.0.zip, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): ROBOSRMSMOTE_1.0.0.tgz, r-oldrel (arm64): ROBOSRMSMOTE_1.0.0.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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