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 gmail.com> License: GPL-3 URL: https://github.com/hee6904/gscaLCA NeedsCompilation: no CRAN checks: gscaLCA results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=gscaLCA to link to this page.
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