Model-Based Clustering via Matrix-Variate Mixture Models
Overview
Implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) arXiv:2005.03861. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.
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NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Michael P.B. Gallaugher <michael_gallaugher@baylor.edu>’ The Description field contains <arXiv:2005.03861>. One key advantage of this model is the ability to Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
- NOTE11 OK, 2 NOTE2026-07-21
- ERROR10 OK, 2 NOTE, 1 ERROR2026-07-18
- NOTE11 OK, 2 NOTE2026-05-12
- ERROR10 OK, 2 NOTE, 1 ERROR2026-05-09
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
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- Documented parameters
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- Return-value docs
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People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.0.0Latest2021-06-11 · current release
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2021-06-11
- Total releases
- 1 / 5 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 3.0 KB / 1 file
- Download size
- 9.8 KB
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