Package: MGMM
Title: Missingness-Aware Gaussian Mixture Models
Date: 2026-02-26
Version: 1.0.1.3
Author: Zachary McCaw [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-2006-9828>)
Maintainer: Zachary McCaw <zmccaw@alumni.harvard.edu>
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 complements existing implementations by allowing for
        both missing elements in the input vectors and full (as opposed
        to strictly diagonal) covariance matrices. Estimation 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. "Fitting Gaussian mixture models on incomplete
        data." <doi:10.1186/s12859-022-04740-9>.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LinkingTo: Rcpp, RcppArmadillo
Imports: cluster, glue, methods, mvnfast, plyr, Rcpp (>= 1.0.3), stats
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, withr
VignetteBuilder: knitr
Config/build/clean-inst-doc: false
RoxygenNote: 7.3.3
Config/testthat/edition: 3
Repository: https://zrmacc.r-universe.dev
Date/Publication: 2026-02-26 16:51:29 UTC
RemoteUrl: https://github.com/zrmacc/mgmm
RemoteRef: HEAD
RemoteSha: 4c0eb4f26650b9fb3501cdb5859cca40c1cf23ff
NeedsCompilation: yes
Packaged: 2026-07-04 12:09:18 UTC; root