Package: tseLCA 1.0.2

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.

Authors:Sam Lee [aut, cre, cph], Jay Goodliffe [ctb]