Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Version: 2.1.3.1 Depends: R (≥ 3.3) Imports: methods, Rcpp (≥ 0.11.1), parallel, mgcv, ggplot2, shiny LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, dplyr, htmltools, scales, plyr Published: 2020-10-14 DOI: 10.32614/CRAN.package.VarSelLCM Author: Matthieu Marbac and Mohammed Sedki Maintainer: Mohammed Sedki <mohammed.sedki at u-psud.fr> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: http://varsellcm.r-forge.r-project.org/ NeedsCompilation: yes Citation: VarSelLCM citation info Materials: NEWS In views: Cluster, MissingData CRAN checks: VarSelLCM results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=VarSelLCM to link to this page.
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