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gscaLCA (version 0.0.5)

Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression

Description

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) . 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.

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Version

Install

install.packages('gscaLCA')

Monthly Downloads

175

Version

0.0.5

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Seohee Park

Last Published

June 8th, 2020

Functions in gscaLCA (0.0.5)

gscaLCR

The 2nd and 3rd step of gscaLCA, which are the partitioning and fitting regression
AddHealth

Add Health data about substance use
summary.gscaLCA

Summary of gscaLCA output or gscaLCR output
gscaLCA

Main function of gscaLCA by using fuzzy clustering GSCA
TALIS

Teaching and Learning International Survey