tseLCA: Three-Step Estimation for Latent Class Analysis
Implements BCH (Bolck-Croon-Hagenaars) <doi:10.1093/pan/mph001> and ML (Vermunt's maximum likelihood) <doi:10.1093/pan/mpq025> approaches for three-step estimation of latent class models with covariates and distal outcomes, following Bakk, Tekle & Vermunt (2013) <doi:10.1177/0081175012470644>, Bakk, Oberski & Vermunt (2014) <https://www.jstor.org/stable/24573086>, and Bakk & Kuha (2018) <doi:10.1007/s11336-017-9592-7>. Built on 'multilevLCA' (Lyrvall et al., 2025) <doi:10.1080/00273171.2025.2473935> for Step-1 measurement model estimation, this package extends it with support for Gaussian, Poisson, and binomial distal outcome families. Unlike 'poLCA', which relies on one-step estimation and cannot accommodate a measurement model from a different sample, this package uses a stepwise approach to prevent the structural model from influencing latent class formation. Implements correct sandwich variance estimation that propagates measurement uncertainty from the first-step through classification-error correction in the final step (Bakk, Oberski & Vermunt, 2014). Supports polytomous items and missing data in the measurement model with full information maximum likelihood. A data-generating process replicating the Bakk & Kuha (2018) simulation study is included.
| Version: |
1.0.2 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
multilevLCA, cli |
| Suggests: |
poLCA, testthat (≥ 3.0.0), parallel, knitr, rmarkdown, spelling |
| Published: |
2026-07-11 |
| DOI: |
10.32614/CRAN.package.tseLCA (may not be active yet) |
| Author: |
Sam Lee  [aut,
cre, cph],
Jay Goodliffe [ctb] |
| 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 |
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