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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.

Versions across snapshots

VersionRepositoryFileSize
2.1.3.2 rolling linux/jammy R-4.5 VarSelLCM_2.1.3.2.tar.gz 713.7 KiB
2.1.3.2 rolling linux/noble R-4.5 VarSelLCM_2.1.3.2.tar.gz 727.6 KiB
2.1.3.2 rolling source/ R- VarSelLCM_2.1.3.2.tar.gz 340.2 KiB
2.1.3.2 latest linux/jammy R-4.5 VarSelLCM_2.1.3.2.tar.gz 713.7 KiB
2.1.3.2 latest linux/noble R-4.5 VarSelLCM_2.1.3.2.tar.gz 727.6 KiB
2.1.3.2 latest source/ R- VarSelLCM_2.1.3.2.tar.gz 340.2 KiB
2.1.3.2 2026-04-26 source/ R- VarSelLCM_2.1.3.2.tar.gz 340.2 KiB
2.1.3.2 2026-04-23 source/ R- VarSelLCM_2.1.3.2.tar.gz 340.2 KiB
2.1.3.2 2026-04-09 windows/windows R-4.5 VarSelLCM_2.1.3.2.zip 1.0 MiB
2.1.3.1 2025-04-20 source/ R- VarSelLCM_2.1.3.1.tar.gz 292.8 KiB

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