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.
|Author||Matthieu Marbac and Mohammed Sedki|
|Date of publication||2015-06-10 19:00:07|
|Maintainer||Mohammed Sedki <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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