MGMM

<p>Parameter estimation and classification via Gaussian Mixture Models, allowing
for missingness in the input vectors. See <code><a rd-options="" href="/link/fit.GMM?package=MGMM&version=0.3.1" data-mini-rdoc="MGMM::fit.GMM">fit.GMM</a></code> for estimating
the GMM, and <code><a rd-options="" href="/link/ChooseK?package=MGMM&version=0.3.1" data-mini-rdoc="MGMM::ChooseK">ChooseK</a></code> for selecting the number of clusters. See 
<code><a rd-options="" href="/link/rGMM?package=MGMM&version=0.3.1" data-mini-rdoc="MGMM::rGMM">rGMM</a></code> for simulating data from a GMM.</p>

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>.

Zachary McCaw

Missingness Aware Gaussian Mixture Models

MGMM function

<p>Parameter estimation and classification via Gaussian Mixture Models, allowing
for missingness in the input vectors. See <code><a rd-options='' href='fit.GMM'>fit.GMM</a></code> for estimating
the GMM, and <code><a rd-options='' href='ChooseK'>ChooseK</a></code> for selecting the number of clusters. See 
<code><a rd-options='' href='rGMM'>rGMM</a></code> for simulating data from a GMM.</p>

MGMM: MGMM: Missingness Aware Gaussian Mixture Models

Description

Arguments