Variable Selection for model-based clustering managed by the Latent Class Model. This model analyses mixed-type data (data with continuous and/ or count and/or categorical variables) with missing values (missing at random) by assuming independence between classes. The one-dimensional 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 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 by taking the expectation of the missing values conditionally on the model, its parameters and on the observed variables.
|Author||Matthieu Marbac and Mohammed Sedki|
|Date of publication||2017-10-16 10:16:53 UTC|
|Maintainer||Mohammed Sedki <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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