Missingness Aware Gaussian Mixture Models

Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimating is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Data." .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("MGMM")

0.4.0 by Zachary McCaw, 2 months ago


Browse source code at https://github.com/cran/MGMM


Authors: Zachary McCaw [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports cluster, methods, mvnfast, plyr, Rcpp, stats

Suggests testthat, knitr, rmarkdown, withr

Linking to Rcpp, RcppArmadillo


See at CRAN