The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

tseLCA 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.

tseLCA 1.0.1

CRAN resubmission

• Removed single quotes around acronyms in DESCRIPTION; added explanations of BCH, ML, and LCA.
• Replaced T/F with TRUE/FALSE throughout internal codebase
• Added \value tags to all exported functions missing them, including bk2018_params.
• inst/examples: examples now write to tempdir() instead of the home filespace.
• inst/examples: commented out rm(list = ls()) calls.
• inst/examples: commented out install.packages() calls.

tseLCA 1.0.2

CRAN resubmission

• Title: corrected hyphenated compound capitalization to “Three-Step” per reviewer request.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.