{ "_id": "6a27b2af24555f66ed537729", "Package": "LCPA", "Type": "Package", "Title": "A General Framework for Latent Classify and Profile Analysis", "Version": "1.0.2", "Date": "2026-04-10", "Author": "Haijiang Qin [aut, cre, cph] (ORCID:\n), Lei Guo [aut, cph]\n(ORCID: )", "Authors@R": "c(person(given = \"Haijiang\", \nfamily = \"Qin\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"haijiang133@outlook.com\",\ncomment = c(ORCID = \"0009-0000-6721-5653\")),\nperson(given = \"Lei\",\nfamily = \"Guo\",\nrole = c(\"aut\", \"cph\"),\nemail = \"happygl1229@swu.edu.cn\",\ncomment = c(ORCID = \"0000-0002-8273-3587\")))", "Maintainer": "Haijiang Qin ", "Description": "A unified latent class modeling framework that encompasses\nboth latent class analysis (LCA) and latent profile analysis\n(LPA), offering a one-stop solution for latent class modeling.\nIt implements state-of-the-art parameter estimation methods,\nincluding the expectation–maximization (EM) algorithm, neural\nnetwork estimation (NNE; requires users to have 'Python' and\nits dependent libraries installed on their computer), and\nintegration with 'Mplus' (requires users to have 'Mplus'\ninstalled on their computer). In addition, it provides commonly\nused model fit indices such as the Akaike information criterion\n(AIC) and Bayesian information criterion (BIC), as well as\nclassification accuracy measures such as entropy. The package\nalso includes fully functional likelihood ratio tests (LRT) and\nbootstrap likelihood ratio tests (BLRT) to facilitate model\ncomparison, along with bootstrap-based and observed information\nmatrix-based standard error estimation. Furthermore, it\nsupports the standard three-step approach for LCA, LPA, and\nlatent transition analysis (LTA) with covariates, enabling\ndetailed covariate analysis. Finally, it includes several\nuser-friendly auxiliary functions to enhance interactive\nusability.", "License": "GPL-3", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "NeedsCompilation": "yes", "Collate": "'adjust.response.R' 'adjust.model.R' 'check.response.R'\n'compare.model.R' 'EM.LCA.R' 'EM.LPA.R' 'get.AvePP.R'\n'get.CEP.R' 'get.entropy.R' 'get.fit.index.R'\n'get.Log.Lik.LCA.R' 'get.Log.Lik.LPA.R' 'get.Log.Lik.LTA.R'\n'get.npar.LCA.R' 'get.npar.LPA.R' 'get.npar.LTA.R'\n'get.P.Z.Xn.LCA.R' 'get.P.Z.Xn.LPA.R' 'get.SE.R'\n'install_python_dependencies.R' 'Kmeans.LCA.R' 'LCA.R' 'LCPA.R'\n'logit.R' 'LPA.R' 'LTA.R' 'LRT.test.R' 'LRT.test.Bootstrap.R'\n'LRT.test.VLMR.R' 'Mplus.LCA.R' 'Mplus.LPA.R' 'normalize.R'\n'plotResponse.R' 'RcppExports.R' 'rdirichlet.R' 'S3extract.R'\n'S3plot.R' 'S3print.R' 'S3summary.R' 'S3update.R'\n'sim.correlation.R' 'sim.LCA.R' 'sim.LPA.R' 'sim.LTA.R'\n'tools.R' 'utils.R' 'logpdf_component.R' 'zzz.R'", "Packaged": { "Date": "2026-06-09 05:58:08 UTC", "User": 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