MGMM: Missingness Aware Gaussian Mixture Models

Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package uses an expectation conditional maximization algorithm to obtain maximum likelihood estimates for all model parameters and maximum a posteriori classifications of the input vectors. For additional details, please see McCaw ZR, Julienne H, Aschard H. "MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Data." <doi:10.1101/2019.12.20.884551>.

Version: 0.3.1
Depends: R (≥ 3.5.0)
Imports: cluster, methods, mvnfast, plyr, Rcpp (≥ 1.0.3), stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2020-08-26
Author: Zachary McCaw ORCID iD [aut, cre]
Maintainer: Zachary McCaw <zmccaw at alumni.harvard.edu>
License: GPL-3
NeedsCompilation: yes
CRAN checks: MGMM results

Downloads:

Reference manual: MGMM.pdf
Vignettes: Missingness Aware Gaussian Mixture Models
Package source: MGMM_0.3.1.tar.gz
Windows binaries: r-devel: MGMM_0.3.1.zip, r-devel-UCRT: MGMM_0.3.1.zip, r-release: MGMM_0.3.1.zip, r-oldrel: MGMM_0.3.1.zip
macOS binaries: r-release (arm64): MGMM_0.3.1.tgz, r-release (x86_64): MGMM_0.3.1.tgz, r-oldrel: MGMM_0.3.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=MGMM to link to this page.