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slca: Structural Modeling for Multiple Latent Class Variables

Provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>, Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and any other extended models involving multiple latent class variables.

Version: 1.3.0
Depends: R (≥ 2.10)
Imports: DiagrammeR, magrittr, MASS, Rcpp, stats
LinkingTo: Rcpp
Suggests: testthat (≥ 3.0.0)
Published: 2024-12-13
DOI: 10.32614/CRAN.package.slca
Author: Youngsun Kim ORCID iD [aut, cre], Hwan Chung ORCID iD [aut]
Maintainer: Youngsun Kim <yskstat at gmail.com>
BugReports: https://github.com/kim0sun/slca/issues
License: GPL (≥ 3)
URL: https://kim0sun.github.io/slca/
NeedsCompilation: yes
Citation: slca citation info
Materials: README
CRAN checks: slca results

Documentation:

Reference manual: slca.pdf

Downloads:

Package source: slca_1.3.0.tar.gz
Windows binaries: r-devel: slca_1.3.0.zip, r-release: slca_1.3.0.zip, r-oldrel: slca_1.3.0.zip
macOS binaries: r-release (arm64): slca_1.3.0.tgz, r-oldrel (arm64): slca_1.3.0.tgz, r-release (x86_64): slca_1.3.0.tgz, r-oldrel (x86_64): slca_1.3.0.tgz
Old sources: slca archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=slca to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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