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 onedimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated CompleteData 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.
Package details 


Author  Matthieu Marbac and Mohammed Sedki 
Date of publication  20150610 19:00:07 
Maintainer  Mohammed Sedki <mohammed.sedki@upsud.fr> 
License  GPL (>= 2) 
Version  1.2 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.