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gscaLCA  

Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression
View on CRAN: Click here


Download and install gscaLCA package within the R console
Install from CRAN:
install.packages("gscaLCA")

Install from Github:
library("remotes")
install_github("cran/gscaLCA")

Install by package version:
library("remotes")
install_version("gscaLCA", "0.0.5")



Attach the package and use:
library("gscaLCA")
Maintained by
Seohee Park
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-09
Latest Update: 2020-06-08
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.
How to cite:
Seohee Park (2019). gscaLCA: Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression. R package version 0.0.5, https://cran.r-project.org/web/packages/gscaLCA
Previous versions and publish date:
0.0.2 (2019-10-09 11:00), 0.0.3 (2020-05-25 23:20)
Other packages that cited gscaLCA R package
View gscaLCA citation profile
Other R packages that gscaLCA depends, imports, suggests or enhances
Functions, R codes and Examples using the gscaLCA R package
Some associated functions: AddHealth . TALIS . gscaLCA . gscaLCR . summary.gscaLCA . 
Some associated R codes: AddHealth.R . Boot_ft.R . EST_ft.R . Prevalence.R . RespItemProb.R . TALIS.R . al_gscaLCA.R . gscaLCA.R . gscaLCR.R . print_graph_gscaLCA.R . print_gscaLCA.R . summary.gscaLCA.R . test_binomial.R . test_multinomial.R .  Full gscaLCA package functions and examples
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