NEWS
LCPA 1.0.4 (2026-09-06)
- We gratefully thank Sungbo Sim (
[email protected]) for using LCPA and for the careful, detailed issue reports and suggestions that helped improve this release.
- Unified
LCPA() and LTA() around type.analysis = "XZ" and "ZY". The measurement model is selected by type.model, its estimator by method.model, and its detailed settings by control.model.
- Added ML and BCH three-step analyses for covariate effects on latent groups (XZ) and latent-group effects on continuous or categorical dependent variables (ZY). Regression estimation and standard errors can be analytic, numerical, or bootstrap-based; continuous dependent variables must be standardized before analysis.
- Extended LTA with state-based and complete-path ZY analyses, time-specific or time-invariant effects, optional pooled Step 1 estimation, participant-level bootstrap, and compiled forward-backward evaluation.
- Expanded ZY results to report conditional means, variances, category probabilities, standard errors, confidence intervals, and omnibus Wald tests. Step 3 sandwich standard errors now account for Step 2 CEP estimation uncertainty where applicable, which can change standard errors relative to earlier releases.
- Reimplemented
LRT.test.VLMR() using the robust-sandwich weighted chi-square reference distribution for Mplus TECH11, evaluated by Imhof's method while retaining negative eigenvalue weights. The adjusted LMR uses the general parameter-difference correction from Lo, Mendell, and Rubin (2001).
- Improved the bootstrap likelihood-ratio test so refits inherit the original fitting controls, with sequential stopping and diagnostics for negative bootstrap LRT statistics.
- Corrected the LCA parameter count, two-sided p-values and confidence-interval rounding, CEP orientation and normalization, LCA EM synchronization, polytomous smoothing, and several likelihood, posterior-probability, model-selection, and simulation-label calculations.
- Improved analytic-gradient optimization, boundary and singular-matrix diagnostics, Louis-information and sandwich standard errors, and standardized fitted-object, simulation, progress, and S3 output. Optional SEM backends through
flexmix, Rmixmod, and RMixtComp and the LCPA/LTA documentation and examples were also expanded.
LCPA 1.0.3
- Improved NNE performance and corrected the LPA EM algorithm.
- Corrected documentation errors.
LCPA 1.0.2 (2026-04-10)
- Added
adjust.model() for aligning LCA or LPA solutions and plot() methods for both model types.
- Improved Mplus variable handling and removed unnecessary Mplus statements.
- Improved Python dependency selection and GPU-based neural-network estimation.
- Improved bootstrap likelihood-ratio testing and corrected documentation errors.
LCPA 1.0.1 (2026-02-27)
- Added
use.attention control for neural-network estimation.
- Corrected
control.NNE configuration and LCA parameter names.
LCPA 1.0.0 (2026-01-22)