c(person("Kristy","Robledo", email = "robledo.kristy@gmail.com", role = c("aut", "cre")),person("Ian", "Marschner", role = c("ths")))
Methods for fitting semi-parametric mean and variance models, with normal or censored data. Also extended to allow a regression in the location, scale and shape parameters.
splines, stats, graphics, sn, survival, utils
Kristy Robledo [aut, cre],
Ian Marschner [ths]

Variable Selection for Model-Based Clustering using the
Integrated Complete-Data Likelihood of a Latent Class Model
Variable Selection for model-based clustering by using a mixture model of Gaussian distributions
assuming conditional independence between variables. The algorithm carries out the model
selection by optimizing the MICL criterion which has a closed form for such a distribution.
methods, Rcpp (>= 0.11.1), parallel, mclust
'ICLexact.R' 'RcppExports.R' 'VSLCM_Classes.R'
'VSLCM_initialization.R' 'VarSelLCM.R' 'Print.R' 'Summary.R'
'onAttach.R'

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.
'CheckInputs.R' 'DataCstr.R' 'VSLCMGrlClasses.R' 'ICLexact.R'
'DesignOutput.R' 'Summary.R' 'Print.R' 'VarSelLCM.R'
'RcppExports.R' 'Imputation.R' 'withoutmixture.R'

Variable Selection for Model-Based Clustering of Continuous,
Count, Categorical or Mixed-Type Data Set with Missing Values
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.
methods, Rcpp (>= 0.11.1), parallel, mgcv
'CheckInputs.R' 'DataCstr.R' 'VSLCMGrlClasses.R' 'ICLexact.R'
'DesignOutput.R' 'Summary.R' 'Print.R' 'VarSelLCM.R'
'RcppExports.R' 'Plot.R' 'Imputation.R' 'withoutmixture.R'