MOutliers: Multivariate Outlier Detection Methods

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

Documentation:

Reference manual:	MOutliers.html , MOutliers.pdf
Vignettes:	my-vignette (source, R code)

Downloads:

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

Linking:

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