RMixtComp: Mixture Models with Heterogeneous and (Partially) Missing Data (original) (raw)
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: | 4.1.5 |
|---|---|
| Depends: | RMixtCompUtilities (≥ 4.1.4), R (≥ 3.5.0) |
| Imports: | RMixtCompIO (≥ 4.0.4), ggplot2, plotly, scales |
| Suggests: | testthat, xml2, Rmixmod, knitr, rmarkdown |
| Published: | 2025-06-15 |
| DOI: | 10.32614/CRAN.package.RMixtComp |
| Author: | Vincent Kubicki [aut], Christophe Biernacki [aut], Quentin Grimonprez [aut, cre], Matthieu Marbac-Lourdelle [ctb], Étienne Goffinet [ctb], Serge Iovleff [ctb], Julien Vandaele [ctb] |
| Maintainer: | Quentin Grimonprez |
| 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: | |
| In views: | Cluster, MissingData |
| CRAN checks: | RMixtComp results |
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