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
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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