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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>A General Framework for Latent Class and Profile Analysis</dc:title>
  <dc:title>R package LCPA version 1.0.4</dc:title>
  <dc:description>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.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 4.1.0)</dc:relation>
  <dc:relation>Imports: reticulate, methods, CompQuadForm, clue, ggplot2, tidyr,
dplyr, mvtnorm, Matrix, MASS, MplusAutomation, tidyselect,
numDeriv, nloptr, patchwork, Rcpp, reshape2, scales</dc:relation>
  <dc:relation>LinkingTo: Rcpp, RcppArmadillo</dc:relation>
  <dc:relation>Suggests: flexmix, Rmixmod, RMixtComp, RMixtCompUtilities</dc:relation>
  <dc:creator>Haijiang Qin &lt;haijiang133@outlook.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Haijiang Qin [aut, cre, cph] (ORCID:
    &lt;https://orcid.org/0009-0000-6721-5653&gt;),
  Lei Guo [aut, cph] (ORCID: &lt;https://orcid.org/0000-0002-8273-3587&gt;)</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2026-09-06</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=LCPA</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.LCPA</dc:identifier>
</oai_dc:dc>
