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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 tuwien.ac.at> |
| License: | GPL (≥ 3) |
| URL: | http://cstat.tuwien.ac.at/filz/ |
| 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: mvoutlier_2.1.1.zip, r-release: mvoutlier_2.1.1.zip, r-oldrel: mvoutlier_2.1.1.zip |
| 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 imports: | cellity, DFA.CANCOR, GateFinder |
| Reverse suggests: | ChemoSpecUtils, fPortfolio, GWmodel, mplot, shotGroups |
| Reverse enhances: | cluster |
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