MGMM-package: Missingness-Aware Gaussian Mixture Models
In MGMM: Missingness-Aware Gaussian Mixture Models
MGMM-package R Documentation
Missingness-Aware Gaussian Mixture Models
Description
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) <doi:10.1186/s12859-022-04740-9>.
Author(s)
Maintainer: Zachary McCaw zmccaw@alumni.harvard.edu (ORCID)
MGMM documentation built on Feb. 27, 2026, 1:07 a.m.
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| MGMM-package | R Documentation |
Missingness-Aware Gaussian Mixture Models
Description
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) <doi:10.1186/s12859-022-04740-9>.
Author(s)
Maintainer: Zachary McCaw zmccaw@alumni.harvard.edu (ORCID)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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