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: 4.1.2
Depends: R (≥ 2.10), RMixtCompUtilities (≥ 4.1.2)
Imports: RMixtCompIO (≥ 4.0.4), ggplot2, plotly, scales
Suggests: testthat, xml2, Rmixmod, blockcluster, knitr, ClusVis, markdown
Published: 2020-06-25
Author: Vincent Kubicki [aut], Christophe Biernacki [aut], Quentin Grimonprez [aut, cre], Matthieu Marbac-Lourdelle [ctb], √Čtienne Goffinet [ctb], Serge Iovleff [ctb]
Maintainer: Quentin Grimonprez <quentin.grimonprez 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, https://massiccc.lille.inria.fr/
NeedsCompilation: no
Materials: NEWS
In views: MissingData
CRAN checks: RMixtComp results


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_4.1.2.tar.gz
Windows binaries: r-devel: RMixtComp_4.1.2.zip, r-release: RMixtComp_4.1.2.zip, r-oldrel: RMixtComp_4.1.2.zip
macOS binaries: r-release: RMixtComp_4.1.2.tgz, r-oldrel: RMixtComp_4.1.2.tgz
Old sources: RMixtComp archive


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