Sequential Outlier Identification for Model-Based Clustering
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 2 earlier changesHide 2 earlier changes
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-10
Documentation
- Help pages
- 39 (1 internal)includes a package overview page
- Examples that run
- 100%7 run in a plain check
- Exported functions with examples
- 54%
- Documented parameters
- 97%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing outlierMBC adds 78 packages (47 with compiled code, 73 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, ggplot2 6.0 MB.
CRAN will archive these unless their check problems are fixed by the date shown. This package needs each one to install.
- flexCWMdeadline passed Oct 12
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- 0.0.1Latest2025-05-28 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-05-28
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 215 KB / 6 files
- Download size
- 254 KB
Cite
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