Variable Selection for modelbased clustering managed by the Latent Class Model. This model analyses mixedtype data (data with continuous and/ or count and/or categorical variables) with missing values (missing at random) by assuming independence between classes. The onedimensional marginals of the components follow standard distributions for facilitating both the model interpretation and the model selection. The variable selection is led by an alternated optimization procedure for maximizing the MICL criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of missing values by taking the expectation of the missing values conditionally on the model, its parameters and on the observed variables.
Package details 


Author  Matthieu Marbac and Mohammed Sedki 
Date of publication  20160713 15:42:57 
Maintainer  Mohammed Sedki <[email protected]> 
License  GPL (>= 2) 
Version  2.0 
URL  http://varsellcm.rforge.rproject.org/ 
Package repository  View on RForge 
Installation 
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