Package: VarSelLCM 2.1.4

Mohammed Sedki

VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

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.

Authors:Matthieu Marbac and Mohammed Sedki

VarSelLCM_2.1.4.tar.gz
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VarSelLCM_2.1.4.tgz(r-4.4-x86_64)VarSelLCM_2.1.4.tgz(r-4.4-arm64)VarSelLCM_2.1.4.tgz(r-4.3-x86_64)VarSelLCM_2.1.4.tgz(r-4.3-arm64)
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VarSelLCM.pdf |VarSelLCM.html✨
VarSelLCM/json (API)
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Peer review:

Bug tracker:https://r-forge.r-project.org/projects/varsellcm

On CRAN:

16 exports 1.82 score 52 dependencies 2 dependents 3 mentions 44 scripts 421 downloads

Last updated 5 years agofrom:51301a3430. Checks:OK: 9. Indexed: yes.

Exports:AICARIBICcoefcoefficientsfittedfitted.valuesICLMICLplotpredictprintsummaryVarSelClusterVarSelImputationVarSelShiny

Dependencies:base64encbslibcachemclicolorspacecommonmarkcrayondigestfansifarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigpromisesR6rappdirsRColorBrewerRcppRcppArmadillorlangsassscalesshinysourcetoolstibbleutf8vctrsviridisLitewithrxtable