VarSelLCM: Variable Selection for Model-Based Clustering using the Integrated Complete-Data Likelihood of a Latent Class Model

Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated Complete-Data Likelihood criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of missing values.

Install the latest version of this package by entering the following in R:
install.packages("VarSelLCM")
AuthorMatthieu Marbac and Mohammed Sedki
Date of publication2015-06-10 19:00:07
MaintainerMohammed Sedki <mohammed.sedki@u-psud.fr>
LicenseGPL (>= 2)
Version1.2

View on CRAN

Files

inst
inst/include
inst/include/Algorithm.h
inst/include/ParamContinuous.h
inst/include/DataContinuous.h
inst/include/Data.h
inst/include/XEM.h
inst/include/XEMContinuous.h
inst/include/Param.h
inst/include/AlgorithmContinuous.h
src
src/Makevars
src/AlgorithmContinuous.cpp
src/Algorithm.cpp
src/VarSelLCMmixte.cpp
src/XEM.cpp
src/DataContinuous.cpp
src/XEMContinuous.cpp
src/ParamContinuous.cpp
src/Makevars.win
src/RcppExports.cpp
NAMESPACE
data
data/banknote.rda
R
R/withoutmixture.R R/DataCstr.R R/Print.R R/Imputation.R R/VarSelLCM.R R/DesignOutput.R R/VSLCMGrlClasses.R R/ICLexact.R R/RcppExports.R R/CheckInputs.R R/Summary.R
MD5
DESCRIPTION
man
man/summary-methods.Rd man/VSLCMcriteria-class.Rd man/VarSeLCluster.Rd man/VSLCMresultsContinuous-class.Rd man/VarSelImputation.Rd man/print-methods.Rd man/VarSelLCM-package.Rd man/VSLCMstrategy-class.Rd man/VSLCMmodel-class.Rd man/banknote.Rd man/VSLCMparametersContinuous-class.Rd man/VSLCMdataContinuous-class.Rd man/VSLCMpartitions-class.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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