{
  "_id": "6ab2673b3d719c1f8408d28d",
  "Package": "LCPA",
  "Type": "Package",
  "Title": "A General Framework for Latent Class and Profile Analysis",
  "Version": "1.0.4",
  "Date": "2026-09-2",
  "Author": "Haijiang Qin [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph]\n(ORCID: <https://orcid.org/0000-0002-8273-3587>)",
  "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 <haijiang133@outlook.com>",
  "Description": "Provides a unified framework for finite-mixture latent\nvariable models, including latent class analysis (LCA), latent\nprofile analysis (LPA), latent class/profile analysis with\ncovariates, and latent transition analysis (LTA), within one\nconsistent interface. Estimation methods include the\nexpectation-maximization (EM) algorithm; neural network\nestimation, which requires 'Python' and its dependent\nlibraries; integration with 'Mplus', which requires an\ninstalled copy of 'Mplus'; and stochastic EM (SEM) through the\noptional 'flexmix', 'Rmixmod', and 'RMixtComp' backends.\n'flexmix' and the default 'Rmixmod' path perform configurable\nwarm-up trajectories and promote the best candidates to full\nSEM replications. 'Rmixmod' additionally exposes its native\nstrategy interface, including chained SEM-to-EM estimation,\nwhereas 'RMixtComp' exposes its native SEM and Gibbs controls\nwithout the external warm-up stage. Model assessment includes\nthe Akaike information criterion (AIC), Bayesian information\ncriterion (BIC), Schwarz information criterion (SIC),\nconsistent AIC (CAIC), approximate weight of evidence (AWE),\nsample-size-adjusted BIC (SABIC), entropy, and average\nposterior probabilities. Model-comparison procedures include\nthe ordinary likelihood-ratio test, the Mplus TECH11\nVuong-Lo-Mendell-Rubin and adjusted Lo-Mendell-Rubin tests, and\nfixed-replicate or sequential parametric bootstrap\nlikelihood-ratio tests. Standard errors can be estimated by\nnonparametric bootstrap, numerical observed information, or\nanalytic observed information based on Louis' identity.\nClassification-error-adjusted maximum-likelihood and\nBolck-Croon-Hagenaars three-step methods support covariates\npredicting latent membership, initial-status and transition\nregressions, and latent classes or states predicting continuous\nand categorical external observed dependent variables.\nSimulation, posterior-probability, classification-error,\nextraction, summary, plotting, model-adjustment, and update\nutilities are also provided for reproducible workflows.",
  "License": "GPL-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'three.step.utils.R' 'ZY.utils.R' 'ML.ZY.utils.R'\n'BCH.XZ.utils.R' 'install_python_dependencies.R' 'Kmeans.LCA.R'\n'Kmeans.LPA.R' 'LCA.R' 'ML.XZ.LCPA.R' 'BCH.XZ.LCPA.R'\n'XZ.LCPA.R' 'BCH.ZY.LCPA.R' 'ML.ZY.LCPA.R' 'ZY.LCPA.R' 'LCPA.R'\n'logit.R' 'LPA.R' 'ML.XZ.LTA.R' 'BCH.XZ.LTA.R' 'XZ.LTA.R'\n'BCH.ZY.LTA.R' 'ML.ZY.LTA.R' 'ZY.LTA.R' 'LTA.R' 'LRT.test.R'\n'LRT.test.Bootstrap.R' 'LRT.test.VLMR.R' 'Mplus.LCA.R'\n'Mplus.LPA.R' 'normalize.R' 'plotResponse.R' 'Rmixmod.utils.R'\n'Rmixmod.LCA.R' 'Rmixmod.LPA.R' 'RMixtComp.utils.R'\n'RMixtComp.LCA.R' 'RMixtComp.LPA.R' 'flexmix.utils.R'\n'flexmix.LCA.R' 'flexmix.LPA.R' 'RcppExports.R' 'rdirichlet.R'\n'S3extract.R' 'S3plot.R' 'S3print.R' 'S3summary.R' 'S3update.R'\n'simulation.utils.R' 'sim.correlation.R' 'sim.LCA.R'\n'sim.LPA.R' 'sim.LTA.R' 'tools.R' 'utils.R' 'zzz.R'",
  "Config/roxygen2/version": "8.1.0",
  "Packaged": {
    "Date": "2026-09-22 11:21:45 UTC",
    "User": "root"
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  "Config/pak/sysreqs": "cmake texlive libicu-dev libpng-dev libssl-dev\npython3",
  "Repository": "https://haijiangq.r-universe.dev",
  "Date/Publication": "2026-09-06 22:50:10 UTC",
  "RemoteUrl": "https://github.com/cran/LCPA",
  "RemoteRef": "HEAD",
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  "_user": "haijiangq",
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  "_expires": "2026-12-31T11:32:09.000Z",
  "_created": "2026-09-22T11:21:45.000Z",
  "_published": "2026-09-22T11:32:11.443Z",
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    "adjust.model",
    "adjust.response",
    "check.response",
    "compare.model",
    "extract",
    "get.AvePP",
    "get.CEP",
    "get.entropy",
    "get.fit.index",
    "get.Log.Lik.LCA",
    "get.Log.Lik.LPA",
    "get.Log.Lik.LTA",
    "get.npar.LCA",
    "get.npar.LPA",
    "get.npar.LTA",
    "get.P.Z.Xn.LCA",
    "get.P.Z.Xn.LPA",
    "get.SE",
    "install_python_dependencies",
    "Kmeans.LCA",
    "Kmeans.LPA",
    "LCA",
    "LCPA",
    "logit",
    "LPA",
    "LRT.test",
    "LRT.test.Bootstrap",
    "LRT.test.VLMR",
    "LTA",
    "normalize",
    "plotResponse",
    "rdirichlet",
    "sim.correlation",
    "sim.LCA",
    "sim.LPA",
    "sim.LTA"
  ],
  "_help": [
    {
      "page": "adjust.model",
      "title": "Align Latent Class/Profile Models via Optimal Permutation",
      "topics": [
        "adjust.model"
      ]
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    {
      "page": "adjust.response",
      "title": "Adjust Categorical Response Data for Polytomous Indicators",
      "topics": [
        "adjust.response"
      ]
    },
    {
      "page": "check.response",
      "title": "Validate response matrix against expected polytomous category counts",
      "topics": [
        "check.response"
      ]
    },
    {
      "page": "compare.model",
      "title": "Model Comparison Tool",
      "topics": [
        "compare.model"
      ]
    },
    {
      "page": "extract",
      "title": "S3 Methods: extract",
      "topics": [
        "extract",
        "extract.compare.model",
        "extract.fit.index",
        "extract.LCA",
        "extract.LCPA",
        "extract.LPA",
        "extract.LTA",
        "extract.SE",
        "extract.sim.LCA",
        "extract.sim.LPA",
        "extract.sim.LTA"
      ]
    },
    {
      "page": "get.AvePP",
      "title": "Calculate Average Posterior Probability (AvePP)",
      "topics": [
        "get.AvePP"
      ]
    },
    {
      "page": "get.CEP",
      "title": "Compute Classification Error Probability (CEP) Matrices",
      "topics": [
        "get.CEP"
      ]
    },
    {
      "page": "get.entropy",
      "title": "Calculate Classification Entropy",
      "topics": [
        "get.entropy"
      ]
    },
    {
      "page": "get.fit.index",
      "title": "Calculate Fit Indices",
      "topics": [
        "get.fit.index"
      ]
    },
    {
      "page": "get.Log.Lik.LCA",
      "title": "Calculate Log-Likelihood for Latent Class Analysis",
      "topics": [
        "get.Log.Lik.LCA"
      ]
    },
    {
      "page": "get.Log.Lik.LPA",
      "title": "Calculate Log-Likelihood for Latent Profile Analysis",
      "topics": [
        "get.Log.Lik.LPA"
      ]
    },
    {
      "page": "get.Log.Lik.LTA",
      "title": "Calculate Log-Likelihood for Latent Transition Analysis",
      "topics": [
        "get.Log.Lik.LTA"
      ]
    },
    {
      "page": "get.npar.LCA",
      "title": "Calculate Number of Free Parameters in Latent Class Analysis",
      "topics": [
        "get.npar.LCA"
      ]
    },
    {
      "page": "get.npar.LPA",
      "title": "Calculate Number of Free Parameters in Latent Profile Analysis",
      "topics": [
        "get.npar.LPA"
      ]
    },
    {
      "page": "get.npar.LTA",
      "title": "Calculate Number of Free Parameters in Latent Transition Analysis",
      "topics": [
        "get.npar.LTA"
      ]
    },
    {
      "page": "get.P.Z.Xn.LCA",
      "title": "Compute Posterior Latent Class Probabilities Based on Fixed Parameters",
      "topics": [
        "get.P.Z.Xn.LCA"
      ]
    },
    {
      "page": "get.P.Z.Xn.LPA",
      "title": "Compute Posterior Latent Profile Probabilities Based on Fixed Parameters",
      "topics": [
        "get.P.Z.Xn.LPA"
      ]
    },
    {
      "page": "get.SE",
      "title": "Compute Standard Errors",
      "topics": [
        "get.SE"
      ]
    },
    {
      "page": "install_python_dependencies",
      "title": "Install Required Python Dependencies for Neural Latent Variable Models",
      "topics": [
        "install_python_dependencies"
      ]
    },
    {
      "page": "Kmeans.LCA",
      "title": "Initialize LCA Parameters via K-means Clustering",
      "topics": [
        "Kmeans.LCA"
      ]
    },
    {
      "page": "Kmeans.LPA",
      "title": "Initialize LPA Parameters via K-means Clustering",
      "topics": [
        "Kmeans.LPA"
      ]
    },
    {
      "page": "LCA",
      "title": "Fit Latent Class Analysis Models",
      "topics": [
        "LCA"
      ]
    },
    {
      "page": "LCPA",
      "title": "Three-step latent class/profile analysis",
      "topics": [
        "LCPA"
      ]
    },
    {
      "page": "logit",
      "title": "Compute the Logistic (Sigmoid) Function",
      "topics": [
        "logit"
      ]
    },
    {
      "page": "LPA",
      "title": "Fit Latent Profile Analysis",
      "topics": [
        "LPA"
      ]
    },
    {
      "page": "LRT.test",
      "title": "Likelihood Ratio Test",
      "topics": [
        "LRT.test"
      ]
    },
    {
      "page": "LRT.test.Bootstrap",
      "title": "Bootstrap Likelihood Ratio Test for Latent Class/Profile Models",
      "topics": [
        "LRT.test.Bootstrap"
      ]
    },
    {
      "page": "LRT.test.VLMR",
      "title": "Vuong-Lo-Mendell-Rubin likelihood ratio test",
      "topics": [
        "LRT.test.VLMR"
      ]
    },
    {
      "page": "LTA",
      "title": "Latent Transition Analysis",
      "topics": [
        "LTA"
      ]
    },
    {
      "page": "normalize",
      "title": "Column-wise Z-Score Standardization",
      "topics": [
        "normalize"
      ]
    },
    {
      "page": "plot",
      "title": "S3 Methods: plot",
      "topics": [
        "plot",
        "plot.LCA",
        "plot.LPA"
      ]
    },
    {
      "page": "plotResponse",
      "title": "Visualize Response Distributions with Density Plots",
      "topics": [
        "plotResponse"
      ]
    },
    {
      "page": "print",
      "title": "S3 Methods: print",
      "topics": [
        "print",
        "print.compare.model",
        "print.fit.index",
        "print.LCA",
        "print.LCPA",
        "print.LPA",
        "print.LTA",
        "print.SE",
        "print.sim.LCA",
        "print.sim.LPA",
        "print.sim.LTA",
        "print.summary.compare.model",
        "print.summary.fit.index",
        "print.summary.LCA",
        "print.summary.LCPA",
        "print.summary.LPA",
        "print.summary.LTA",
        "print.summary.SE",
        "print.summary.sim.LCA",
        "print.summary.sim.LPA",
        "print.summary.sim.LTA"
      ]
    },
    {
      "page": "rdirichlet",
      "title": "Generate Random Samples from the Dirichlet Distribution",
      "topics": [
        "rdirichlet"
      ]
    },
    {
      "page": "sim.correlation",
      "title": "Generate a Random Correlation Matrix via C-Vine Partial Correlations",
      "topics": [
        "sim.correlation"
      ]
    },
    {
      "page": "sim.LCA",
      "title": "Simulate Data for Latent Class Analysis",
      "topics": [
        "sim.LCA"
      ]
    },
    {
      "page": "sim.LPA",
      "title": "Simulate Data for Latent Profile Analysis",
      "topics": [
        "sim.LPA"
      ]
    },
    {
      "page": "sim.LTA",
      "title": "Simulate Data for Latent Transition Analysis (LTA)",
      "topics": [
        "sim.LTA"
      ]
    },
    {
      "page": "summary",
      "title": "S3 Methods: summary",
      "topics": [
        "summary",
        "summary.compare.model",
        "summary.fit.index",
        "summary.LCA",
        "summary.LCPA",
        "summary.LPA",
        "summary.LTA",
        "summary.SE",
        "summary.sim.LCA",
        "summary.sim.LPA",
        "summary.sim.LTA"
      ]
    },
    {
      "page": "update",
      "title": "S3 Methods: update",
      "topics": [
        "update",
        "update.LCA",
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