LCAvarsel - Variable Selection for Latent Class Analysis
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. (2017)
<doi:10.1214/17-AOAS1061> 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.
