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: 0.0.3
Depends: R (≥ 2.10)
Imports: gridExtra, ggplot2, stringr, progress, psych, fastDummies, fclust, MASS, devtools, foreach, doSNOW, nnet
Suggests: knitr, rmarkdown
Published: 2020-05-25
Author: Jihoon Ryoo [aut], Seohee Park [aut, cre], Seoungeun Kim [aut], heungsun Hwaung [aut]
Maintainer: Seohee Park <hee6904 at gmail.com>
License: GPL-3
URL: https://github.com/hee6904/gscaLCA
NeedsCompilation: no
CRAN checks: gscaLCA results

Downloads:

Reference manual: gscaLCA.pdf
Package source: gscaLCA_0.0.3.tar.gz
Windows binaries: r-devel: gscaLCA_0.0.3.zip, r-release: gscaLCA_0.0.3.zip, r-oldrel: gscaLCA_0.0.3.zip
macOS binaries: r-release: gscaLCA_0.0.2.tgz, r-oldrel: gscaLCA_0.0.3.tgz
Old sources: gscaLCA archive

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