MGMM-package

MGMM

_PACKAGE

Estimate and classify using Gaussian mixture models (GMMs) when the input
contains missing values. Uses an expectation–conditional maximization (ECM)
algorithm with support for full component covariances and optional ridge
regularization. See McCaw et al. (2022) &lt;doi:10.1186/s12859-022-04740-9&gt;.

internal

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>.

Zachary McCaw

Missingness-Aware Gaussian Mixture Models

MGMM-package function

Maintainer: Zachary McCaw <a href="/link/zmccaw%40alumni.harvard.edu?package=MGMM&version=1.0.1.3" data-mini-rdoc="MGMM::zmccaw@alumni.harvard.edu">zmccaw@alumni.harvard.edu</a> (<a href="https://orcid.org/0000-0002-2006-9828">ORCID</a>)

Author

Missingness-Aware Gaussian Mixture Models — MGMM-package

Maintainer: Zachary McCaw <a href='mailto:zmccaw@alumni.harvard.edu'>zmccaw@alumni.harvard.edu</a> (<a href='https://orcid.org/0000-0002-2006-9828'>ORCID</a>)

MGMM-package: Missingness-Aware Gaussian Mixture Models

Description

Arguments

Author