multimix

A set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) doi:10.1111/j.2517-6161.1977.tb01600.x Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1–22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) doi:10.1111/1467-842X.00071 Australian & New Zealand Journal of Statistics 41(2), 153–171 and Hunt, L. and Jorgensen, M. (2003) doi:10.1016/S0167-9473(02)00190-1 Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429–440.

Last 30 days

This package has been downloaded 218 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 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 2,745 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 Jul 21, 2024 with 71 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.