Title: Variable Selection for Model-Based Clustering of Continuous,
Count, Categorical or Mixed-Type Data Set with Missing Values
Description: 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.
Imports: methods, Rcpp (>= 0.11.1), parallel, mgcv

Title: Variable Selection for Model-Based Clustering of Mixed-Type Data
Description: Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering<doi:10.1007/s11222-016-9670-1>. Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Imports: methods, Rcpp (>= 0.11.1), parallel, mgcv, ggplot2, shiny
Collate: 'CheckInputs.R' 'data.R' 'model.R' 'param.R' 'results.R'

Description: Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Suggests: knitr, rmarkdown, dplyr, htmltools, scales, plyr