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

Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. It includes 8 models for real, categorical, counting, functional and ranking data.

Version: 4.1.3
Depends: R (≥ 2.10), RMixtCompUtilities (≥ 4.1.4)
Imports: RMixtCompIO (≥ 4.0.4), ggplot2, plotly, scales
Suggests: testthat, xml2, Rmixmod, blockcluster, knitr, ClusVis, rmarkdown
Published: 2021-03-29
Author: Vincent Kubicki [aut], Christophe Biernacki [aut], Quentin Grimonprez [aut], Matthieu Marbac-Lourdelle [ctb], √Čtienne Goffinet [ctb], Serge Iovleff [ctb], Julien Vandaele [ctb, cre]
Maintainer: Julien Vandaele <julien.vandaele at inria.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: MissingData
CRAN checks: RMixtComp results

Downloads:

Reference manual: RMixtComp.pdf
Vignettes: Using ClusVis with RMixtComp Output for Visualization
Using RMixtComp with mixed and missing data
Data Format
MixtComp Object
Package source: RMixtComp_4.1.3.tar.gz
Windows binaries: r-devel: RMixtComp_4.1.3.zip, r-release: RMixtComp_4.1.3.zip, r-oldrel: RMixtComp_4.1.3.zip
macOS binaries: r-release: RMixtComp_4.1.3.tgz, r-oldrel: RMixtComp_4.1.3.tgz
Old sources: RMixtComp archive

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