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LCAvarsel  

Variable Selection for Latent Class Analysis
View on CRAN: Click here


Download and install LCAvarsel package within the R console
Install from CRAN:
install.packages("LCAvarsel")

Install from Github:
library("remotes")
install_github("cran/LCAvarsel")

Install by package version:
library("remotes")
install_version("LCAvarsel", "1.1")



Attach the package and use:
library("LCAvarsel")
Maintained by
Michael Fop
[Scholar Profile | Author Map]
First Published: 2017-11-20
Latest Update: 2018-01-04
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. (2017) and Dean and Raftery (2010) . 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.
How to cite:
Michael Fop (2017). LCAvarsel: Variable Selection for Latent Class Analysis. R package version 1.1, https://cran.r-project.org/web/packages/LCAvarsel. Accessed 01 Apr. 2025.
Previous versions and publish date:
1.0 (2017-11-20 19:08)
Other packages that cited LCAvarsel R package
View LCAvarsel citation profile
Other R packages that LCAvarsel depends, imports, suggests or enhances
Complete documentation for LCAvarsel
Functions, R codes and Examples using the LCAvarsel R package
Some associated functions: LCAvarsel . compareCluster . control-parameters . fitLCA . internal-functions . maxG . 
Some associated R codes: LCAvarsel.R . control.R . fitLCA.R . regressionStep.R . selBWD.R . selFWD.R . selGA.R . utils.R . zzz.R .  Full LCAvarsel package functions and examples
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