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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.5 |
rolling source/ R- | gscaLCA_0.0.5.tar.gz |
62.3 KiB |
0.0.5 |
rolling linux/jammy R-4.5 | gscaLCA_0.0.5.tar.gz |
128.9 KiB |
0.0.5 |
rolling linux/noble R-4.5 | gscaLCA_0.0.5.tar.gz |
128.9 KiB |
0.0.5 |
latest source/ R- | gscaLCA_0.0.5.tar.gz |
62.3 KiB |
0.0.5 |
latest linux/jammy R-4.5 | gscaLCA_0.0.5.tar.gz |
128.9 KiB |
0.0.5 |
latest linux/noble R-4.5 | gscaLCA_0.0.5.tar.gz |
128.9 KiB |
0.0.5 |
2026-04-23 source/ R- | gscaLCA_0.0.5.tar.gz |
62.3 KiB |
0.0.5 |
2026-04-09 windows/windows R-4.5 | gscaLCA_0.0.5.zip |
132.2 KiB |
0.0.5 |
2025-04-20 source/ R- | gscaLCA_0.0.5.tar.gz |
62.3 KiB |