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MGMM

1.0.1.3

Missingness-Aware Gaussian Mixture Models

0packages depend
3.9Kdownloads / year
95.9%test coverage
13/13checks pass

Overview

About
Maintained by Zachary McCawFirst published 2020-08-266 releasesCRAN page ↗

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.

Install

Health

CRAN checks
13OK
Slowest check: 7.6 min · r-devel-windows-x86_64
Code health
Yes
Tests · ratio 0.23
95.9%
Coverage · measured lines
100%
Documentation · exports
7
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-04-22
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 191 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 33% structuredCode of conductNoContributing guideNo
Examples that run
67%
Documented parameters
100%
Return-value docs
100%
References docs
0%

Downloads

3.9K
CRAN downloads in the past year
Rank #11,855 · ~11/day · ~323/mo
Daily download trend is not available in this view yet.
18930 days
83390 days
3.9K1 year
Compare downloads with other packages →
Also on412 r2u25 autocran54 c2d4u

Dependencies

Declared dependencies
10 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5.0
Imports (7)
clustergluemethodsmvnfastplyrRcppstats
LinkingTo (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (1)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Package Timeline

6 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.0.1.3Latest
    2026-02-26 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 1.0.1.1
    2023-09-30 · diff ↗
  • 1.0.1
    2023-08-08 · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • 1.0.0
    2021-12-21 · diff ↗
  • 0.4.0
    2021-07-25 · diff ↗
  • R
    R 4.1.0 released · 2021-05-18
  • 0.3.1
    2020-08-26
  • R
    R 4.0.0 released · 2020-04-24

Package metadata

First published
2020-08-26
Total releases
6 / 6 yrs
License
GPL-3 OSI
Minimum R
≥ 3.5.0
Download size
447 KB
Installed size
not tracked yet
With dependencies
not tracked yet
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("MGMM")
McCaw, Z. (2026). MGMM: Missingness-Aware Gaussian Mixture Models (Version 1.0.1.3) [Computer software]. https://doi.org/10.32614/CRAN.package.MGMM

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for MGMM version 1.0.1.3 [Data set]. HJJB, LLC. Data release v2026-08-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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