tseLCA - Three-Step Estimation for Latent Class Analysis

Implements 'BCH' (Bolck, Croon & Hagenaars, 2004) <doi:10.1093/pan/mph001> and 'ML' (Vermunt, 2010) <doi:10.1093/pan/mpq025> three-step estimators for latent class analysis ('LCA') 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 'LCA' through classification-error correction in the final step (Bakk, Oberski & Vermunt, 2014). Supports polytomous items and missing data in the measurement model with 'FIML'. A data-generating process replicating the Bakk & Kuha (2018) simulation study is included.