gscaLCA: Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression

Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>. It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.

Version: 0.0.5
Depends: R (≥ 2.10)
Imports: gridExtra, ggplot2, stringr, progress, psych, fastDummies, fclust, MASS, devtools, foreach, doSNOW, nnet
Suggests: knitr, rmarkdown
Published: 2020-06-08
DOI: 10.32614/CRAN.package.gscaLCA
Author: Jihoon Ryoo [aut], Seohee Park [aut, cre], Seoungeun Kim [aut], heungsun Hwaung [aut]
Maintainer: Seohee Park <hee6904 at>
License: GPL-3
NeedsCompilation: no
CRAN checks: gscaLCA results


Reference manual: gscaLCA.pdf


Package source: gscaLCA_0.0.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): gscaLCA_0.0.5.tgz, r-oldrel (arm64): gscaLCA_0.0.5.tgz, r-release (x86_64): gscaLCA_0.0.5.tgz, r-oldrel (x86_64): gscaLCA_0.0.5.tgz
Old sources: gscaLCA archive


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