outlierMBC: Sequential Outlier Identification for Model-Based Clustering
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 
[aut, cre, cph],
Paul D. McNicholas
[aut],
Arthur White
[aut] |
| Maintainer: |
Ultán P. Doherty <dohertyu at tcd.ie> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Citation: |
outlierMBC citation info |
| Materials: |
README |
| In views: |
AnomalyDetection |
| CRAN checks: |
outlierMBC results |
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