MatrixMixtures
Model-Based Clustering via Matrix-Variate Mixture Models
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | MatrixMixtures_1.0.0.tar.gz |
69.2 KiB |
1.0.0 |
rolling linux/noble R-4.5 | MatrixMixtures_1.0.0.tar.gz |
69.1 KiB |
1.0.0 |
rolling source/ R- | MatrixMixtures_1.0.0.tar.gz |
10.1 KiB |
1.0.0 |
latest linux/jammy R-4.5 | MatrixMixtures_1.0.0.tar.gz |
69.2 KiB |
1.0.0 |
latest linux/noble R-4.5 | MatrixMixtures_1.0.0.tar.gz |
69.1 KiB |
1.0.0 |
latest source/ R- | MatrixMixtures_1.0.0.tar.gz |
10.1 KiB |
1.0.0 |
2026-04-26 source/ R- | MatrixMixtures_1.0.0.tar.gz |
10.1 KiB |
1.0.0 |
2026-04-23 source/ R- | MatrixMixtures_1.0.0.tar.gz |
10.1 KiB |
1.0.0 |
2026-04-09 windows/windows R-4.5 | MatrixMixtures_1.0.0.zip |
72.7 KiB |
1.0.0 |
2025-04-20 source/ R- | MatrixMixtures_1.0.0.tar.gz |
10.1 KiB |