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 |
| 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 |
| Reference manual: | gscaLCA.pdf |
| Package source: | gscaLCA_0.0.5.tar.gz |
| Windows binaries: | r-devel: gscaLCA_0.0.5.zip, r-release: gscaLCA_0.0.5.zip, r-oldrel: gscaLCA_0.0.5.zip |
| macOS binaries: | r-release: gscaLCA_0.0.5.tgz, r-oldrel: gscaLCA_0.0.5.tgz |
| Old sources: | gscaLCA archive |
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