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 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.