Package: LCAvarsel
Type: Package
Title: Variable Selection for Latent Class Analysis
Description: Variable selection for latent class analysis for model-based clustering of multivariate categorical data.
     The package implements a general framework for selecting the subset of variables with relevant clustering information
     and discard those that are redundant and/or not informative. The variable selection method is based on the approach
     of Fop et al. (2015) <arXiv:1512.03350> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>.
     Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search.
     Concomitant covariates used to predict the class membership probabilities can also be included
     in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.
Version: 1.0
Date: 2017-11-19
Authors@R: c( person("Michael", "Fop", role = c("aut", "cre"), email = "michael.fop@ucd.ie"),
             person("Thomas Brendan", "Murphy", role = "ctb", email = "brendan.murphy@ucd.ie") )
Author: Michael Fop [aut, cre],
        Thomas Brendan Murphy [ctb]
Maintainer: Michael Fop <michael.fop@ucd.ie>
URL: https://michaelfop.github.io/
Depends: R (>= 3.4), poLCA (>= 1.4.1)
License: GPL (>= 2)
Imports: nnet, MASS, foreach, parallel, doParallel, GA, memoise
Suggests: knitr (>= 1.12), rmarkdown (>= 1.2)
ByteCompile: true
LazyData: true
NeedsCompilation: no
Packaged: 2017-11-20 15:28:18 UTC; michael
Repository: CRAN
Date/Publication: 2017-11-20 18:08:27 UTC
