MatrixMixtures

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) doi:10.48550/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.

Last 30 days

This package has been downloaded 143 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,199 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 25 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.