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.