Package: MGMM
Title: Missingness Aware Gaussian Mixture Models
Date: 2020-08-24
Version: 0.3.1
Authors@R: 
    person(given = "Zachary",
           family = "McCaw",
           role = c("aut", "cre"),
           email = "zmccaw@alumni.harvard.edu",
           comment = c(ORCID = "0000-0002-2006-9828"))
Description: 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>.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
Imports: cluster, methods, mvnfast, plyr, Rcpp (>= 1.0.3), stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-08-25 13:36:53 UTC; zmccaw
Author: Zachary McCaw [aut, cre] (<https://orcid.org/0000-0002-2006-9828>)
Maintainer: Zachary McCaw <zmccaw@alumni.harvard.edu>
Repository: CRAN
Date/Publication: 2020-08-26 11:50:02 UTC
Built: R 4.0.2; x86_64-apple-darwin17.0; 2020-08-27 10:31:43 UTC; unix
Archs: MGMM.so.dSYM
