Provides methods for detecting multivariate outliers in numeric datasets. The package implements classical Mahalanobis distance, robust Minimum Covariance Determinant (MCD), and Principal Component Analysis (PCA)-based approaches for outlier detection. The methodology is informed by Aggarwal (2017) <doi:10.1007/978-3-319-47578-3> and Grentzelos, Caroni and Barranco-Chamorro (2020) <doi:10.1002/cmm4.1129>. Visualization functions are included to aid interpretation of detected outliers. Mahalanobis distance calculations are accelerated using 'C++' through 'Rcpp'.
| Version: | 0.1.2 |
| Imports: | Rcpp, stats, MASS, ggplot2, gridExtra, cowplot, rlang |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-06-16 |
| DOI: | 10.32614/CRAN.package.MOutliers |
| Author: | Senuri Yasara [aut, cre], Pavanthi Sudasinghe [aut] |
| Maintainer: | Senuri Yasara <senuriyasara at gmail.com> |
| BugReports: | https://github.com/SenuYasara/Multivariate_Outlier_Detection_R_Package/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/SenuYasara/Multivariate_Outlier_Detection_R_Package |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | MOutliers results |
| Reference manual: | MOutliers.html , MOutliers.pdf |
| Vignettes: |
my-vignette (source, R code) |
| Package source: | MOutliers_0.1.2.tar.gz |
| Windows binaries: | r-devel: MOutliers_0.1.2.zip, r-release: MOutliers_0.1.2.zip, r-oldrel: MOutliers_0.1.2.zip |
| macOS binaries: | r-release (arm64): MOutliers_0.1.2.tgz, r-oldrel (arm64): MOutliers_0.1.2.tgz, r-release (x86_64): MOutliers_0.1.2.tgz, r-oldrel (x86_64): MOutliers_0.1.2.tgz |
| Old sources: | MOutliers archive |
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