Changes in version 1.0.0                        

  - Initial submission to CRAN.

Core Estimation Framework

  - 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.

Measurement Model (Step 1) Integration

  - 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.

Algorithmic Flexibility & Structural Models

  - 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.

Utilities and Methods

  - 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.