R/00_About.R

In zrmacc/MGMM: Missingness Aware Gaussian Mixture Models

# Purpose: Package documentation.
# Updated: 2021-07-25

#' @useDynLib MGMM, .registration = TRUE
#' @importFrom Rcpp sourceCpp
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#' MGMM: 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. 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. "MGMM: an R
#' package for fitting Gaussian Mixture Models on Incomplete Data."
#' <doi:10.1101/2019.12.20.884551>.
#' 
#' @author Zachary R. McCaw
#' @docType package
#' @name MGMM
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