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MGMM  

Missingness Aware Gaussian Mixture Models
View on CRAN: Click here


Download and install MGMM package within the R console
Install from CRAN:
install.packages("MGMM")

Install from Github:
library("remotes")
install_github("cran/MGMM")

Install by package version:
library("remotes")
install_version("MGMM", "1.0.1.1")



Attach the package and use:
library("MGMM")
Maintained by
Zachary McCaw
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-08-26
Latest Update: 2023-09-30
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." .
How to cite:
Zachary McCaw (2020). MGMM: Missingness Aware Gaussian Mixture Models. R package version 1.0.1.1, https://cran.r-project.org/web/packages/MGMM. Accessed 23 Jul. 2026.
Previous versions and publish date:
0.3.1 (2020-08-26 13:50), 0.4.0 (2021-07-25 16:50), 1.0.0 (2021-12-21 19:12), 1.0.1.1 (2023-09-30 17:42), 1.0.1 (2023-08-08 15:50), (2026-07-09 08:09)
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