RMixtComp: Mixture Models with Heterogeneous and (Partially) Missing Data

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.

Version: 4.1.4
Depends: RMixtCompUtilities (≥ 4.1.4), R (≥ 3.5.0)
Imports: RMixtCompIO (≥ 4.0.4), ggplot2, plotly, scales
Suggests: testthat, xml2, Rmixmod, knitr, ClusVis, rmarkdown
Published: 2023-06-18
Author: Vincent Kubicki [aut], Christophe Biernacki [aut], Quentin Grimonprez [aut, cre], Matthieu Marbac-Lourdelle [ctb], √Čtienne Goffinet [ctb], Serge Iovleff [ctb], Julien Vandaele [ctb]
Maintainer: Quentin Grimonprez <quentingrim at yahoo.fr>
BugReports: https://github.com/modal-inria/MixtComp/issues
License: AGPL-3
Copyright: Inria - Université de Lille - CNRS
URL: https://github.com/modal-inria/MixtComp
NeedsCompilation: no
Materials: NEWS
In views: Cluster, MissingData
CRAN checks: RMixtComp results

Documentation:

Reference manual: RMixtComp.pdf
Vignettes: Using ClusVis with RMixtComp Output for Visualization
Using RMixtComp with mixed and missing data
Data format used in RMixtComp
Overview of MixtComp Object

Downloads:

Package source: RMixtComp_4.1.4.tar.gz
Windows binaries: r-devel: RMixtComp_4.1.4.zip, r-release: RMixtComp_4.1.4.zip, r-oldrel: RMixtComp_4.1.4.zip
macOS binaries: r-release (arm64): RMixtComp_4.1.4.tgz, r-oldrel (arm64): RMixtComp_4.1.4.tgz, r-release (x86_64): RMixtComp_4.1.4.tgz, r-oldrel (x86_64): RMixtComp_4.1.4.tgz
Old sources: RMixtComp archive

Linking:

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