NEWS

tseLCA 1.0.0

Implemented BCH and ML bias-adjusted three-step estimators for latent class analysis (LCA).
Added support for structural models containing covariates ($Z_p$), distal outcomes ($Z_o$), and combined models (estimating the relationship between $Z_p$ and the latent class first, followed by the distal outcome adjusting for covariate-adjusted posteriors).
Implemented analytic sandwich variance estimation to correctly propagate measurement uncertainty from the first-step LCA through classification-error correction in the final step.
Added a robust standard error option (use.simple.cov = TRUE) that bypasses the measurement-uncertainty correction for faster computation in large, well-separated samples.

Integrated with the 'multilevLCA' package for efficient Step-1 measurement model estimation.
Added support for polytomous indicator items (0-based integer coding).
Implemented Full Information Maximum Likelihood (FIML) to handle missing data in the measurement model via the incomplete = TRUE argument (using a two-pass row-filtering strategy).
Added the ability to pass a pre-fitted measurement model (via the step1 argument) to reuse across multiple structural models or apply to different sample subsets.
Implemented automated random restarts for the measurement model triggered when entropy $R^2$ falls below a user-specified threshold.

Added support for both modal and proportional (soft) posterior class assignment (use.modal.assignment).
Integrated Gaussian, Poisson, and binomial families for distal outcome estimation.
Added the rebase argument to allow users to easily change the reference latent class for the multinomial logit parameterization while maintaining invariant log-likelihoods.
Implemented two-step EM estimation (fitZ_from_fit0()) to generate stable starting values for the three-step structural model.

Included standard S3 methods for tseLCA objects: summary(), coef(), vcov(), and plot() (which delegates to 'multilevLCA' for item-profile visualization).
Built a data-generating process (generate_data()) that replicates the Bakk & Kuha (2018) simulation study design for both covariates and distal outcomes under varying separation conditions.