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Sequential outlier identification for Gaussian mixture models using the distribution of Mahalanobis distances. The optimal number of outliers is chosen based on the dissimilarity between the theoretical and observed distributions of the scaled squared sample Mahalanobis distances. Also includes an extension for Gaussian linear cluster-weighted models using the distribution of studentized residuals. Doherty, McNicholas, and White (2025) <doi:10.48550/arXiv.2505.11668>.
Version: | 0.0.1 |
Depends: | R (≥ 4.1.0) |
Imports: | ClusterR, dbscan, flexCWM, ggplot2, mixture, mvtnorm, spatstat.univar, stats |
Published: | 2025-05-28 |
DOI: | 10.32614/CRAN.package.outlierMBC |
Author: | Ultán P. Doherty |
Maintainer: | Ultán P. Doherty <dohertyu at tcd.ie> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | outlierMBC citation info |
Materials: | README |
CRAN checks: | outlierMBC results |
Reference manual: | outlierMBC.pdf |
Package source: | outlierMBC_0.0.1.tar.gz |
Windows binaries: | r-devel: outlierMBC_0.0.1.zip, r-release: outlierMBC_0.0.1.zip, r-oldrel: outlierMBC_0.0.1.zip |
macOS binaries: | r-release (arm64): outlierMBC_0.0.1.tgz, r-oldrel (arm64): outlierMBC_0.0.1.tgz, r-release (x86_64): outlierMBC_0.0.1.tgz, r-oldrel (x86_64): outlierMBC_0.0.1.tgz |
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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.