tseLCA: Three-Step Estimation for Latent Class Analysis
Bias-adjusted three-step estimation of latent class models with
covariates and distal outcomes. The latent class measurement model is
estimated first, with 'multilevLCA' (Lyrvall et al., 2025)
<doi:10.1080/00273171.2025.2473935>, and held fixed; observations are then
classified; and the classes are related to covariates and distal outcomes
with the maximum likelihood correction of Vermunt (2010)
<doi:10.1093/pan/mpq025> and Bakk, Tekle and Vermunt (2013)
<doi:10.1177/0081175012470644>, or the correction of Bolck, Croon and
Hagenaars (2004) <doi:10.1093/pan/mph001>. Standard errors account for the
uncertainty of the measurement model (Bakk, Oberski and Vermunt, 2014)
<doi:10.1093/pan/mpu003>. Includes class enumeration, modal and
proportional class assignment, covariate formulas, Gaussian, Poisson,
binomial, and multinomial distal outcomes, the two-step estimator of Bakk
and Kuha (2018) <doi:10.1007/s11336-017-9592-7>, measurement models
applied to new samples, and full-information maximum likelihood for
missing indicators, standard methods for fitted models, and
a data-generating process replicating the simulation design of Bakk and
Kuha (2018).
| Version: |
2.0.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
cli, Formula, multilevLCA |
| Suggests: |
nnet, poLCA, testthat (≥ 3.0.0), parallel, knitr, rmarkdown, spelling |
| Published: |
2026-10-01 |
| DOI: |
10.32614/CRAN.package.tseLCA |
| Author: |
Sam Lee  [aut,
cre, cph],
Jay Goodliffe [aut, cph] |
| Maintainer: |
Sam Lee <samlee at arizona.edu> |
| BugReports: |
https://github.com/SamLeeBYU/tseLCA/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://samleebyu.github.io/tseLCA/,
https://github.com/SamLeeBYU/tseLCA |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
tseLCA citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
tseLCA results |
Documentation:
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