LCPA: A General Framework for Latent Class and Profile Analysis
Provides a unified framework for finite-mixture latent variable
models, including latent class analysis (LCA), latent profile analysis (LPA),
latent class/profile analysis with covariates, and latent transition analysis
(LTA), within one consistent interface. Estimation methods include the
expectation-maximization (EM) algorithm; neural network estimation, which
requires 'Python' and its dependent libraries; integration with 'Mplus',
which requires an installed copy of 'Mplus'; and stochastic EM (SEM) through
the optional 'flexmix', 'Rmixmod', and 'RMixtComp' backends. 'flexmix' and the
default 'Rmixmod' path perform configurable warm-up trajectories and promote
the best candidates to full SEM replications. 'Rmixmod' additionally exposes
its native strategy interface, including chained SEM-to-EM estimation,
whereas 'RMixtComp' exposes its native SEM and Gibbs controls without the
external warm-up stage. Model assessment includes the Akaike information
criterion (AIC), Bayesian information criterion (BIC), Schwarz information
criterion (SIC), consistent AIC (CAIC), approximate weight of evidence (AWE),
sample-size-adjusted BIC (SABIC), entropy, and average posterior
probabilities. Model-comparison procedures include the ordinary
likelihood-ratio test, the Mplus TECH11 Vuong-Lo-Mendell-Rubin and adjusted
Lo-Mendell-Rubin tests, and fixed-replicate or sequential parametric bootstrap
likelihood-ratio tests. Standard errors can be estimated by nonparametric
bootstrap, numerical observed information, or analytic observed information
based on Louis' identity. Classification-error-adjusted maximum-likelihood
and Bolck-Croon-Hagenaars three-step methods support covariates predicting
latent membership, initial-status and transition regressions, and latent
classes or states predicting continuous and categorical external observed
dependent variables. Simulation,
posterior-probability, classification-error,
extraction, summary, plotting, model-adjustment, and update utilities are
also provided for reproducible workflows.
| Version: |
1.0.4 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
reticulate, methods, CompQuadForm, clue, ggplot2, tidyr, dplyr, mvtnorm, Matrix, MASS, MplusAutomation, tidyselect, numDeriv, nloptr, patchwork, Rcpp, reshape2, scales |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
flexmix, Rmixmod, RMixtComp, RMixtCompUtilities |
| Published: |
2026-09-06 |
| DOI: |
10.32614/CRAN.package.LCPA |
| Author: |
Haijiang Qin 
[aut, cre, cph],
Lei Guo [aut,
cph] |
| Maintainer: |
Haijiang Qin <haijiang133 at outlook.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
LCPA results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=LCPA
to link to this page.