mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.

Version: 2.1.1
Depends: sgeostat, R (≥ 3.1)
Imports: robustbase
Published: 2021-07-30
DOI: 10.32614/CRAN.package.mvoutlier
Author: Peter Filzmoser and Moritz Gschwandtner
Maintainer: P. Filzmoser <P.Filzmoser at>
License: GPL (≥ 3)
NeedsCompilation: no
In views: Robust
CRAN checks: mvoutlier results


Reference manual: mvoutlier.pdf


Package source: mvoutlier_2.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvoutlier_2.1.1.tgz, r-oldrel (arm64): mvoutlier_2.1.1.tgz, r-release (x86_64): mvoutlier_2.1.1.tgz, r-oldrel (x86_64): mvoutlier_2.1.1.tgz
Old sources: mvoutlier archive

Reverse dependencies:

Reverse imports: cellity, GateFinder
Reverse suggests: ChemoSpecUtils, fPortfolio, GWmodel, mplot, shotGroups
Reverse enhances: cluster


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