{
  "_id": "6a1d62ca1d7bb097a0a4b1ac",
  "Package": "fabMix",
  "Type": "Package",
  "Title": "Overfitting Bayesian Mixtures of Factor Analyzers with\nParsimonious Covariance and Unknown Number of Components",
  "Version": "5.1",
  "Date": "2024-02-12",
  "Authors@R": "c(person(given = \"Panagiotis\",\nfamily = \"Papastamoulis\",\nemail = \"papapast@yahoo.gr\",\nrole = c( \"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9468-7613\")))",
  "Maintainer": "Panagiotis Papastamoulis <papapast@yahoo.gr>",
  "Description": "Model-based clustering of multivariate continuous data\nusing Bayesian mixtures of factor analyzers (Papastamoulis\n(2019) <DOI:10.1007/s11222-019-09891-z> (2018)\n<DOI:10.1016/j.csda.2018.03.007>). The number of clusters is\nestimated using overfitting mixture models (Rousseau and\nMengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>):\nsuitable prior assumptions ensure that asymptotically the extra\ncomponents will have zero posterior weight, therefore, the\ninference is based on the ``alive'' components. A Gibbs sampler\nis implemented in order to (approximately) sample from the\nposterior distribution of the overfitting mixture. A prior\nparallel tempering scheme is also available, which allows to\nrun multiple parallel chains with different prior distributions\non the mixture weights. These chains run in parallel and can\nswap states using a Metropolis-Hastings move. Eight different\nparameterizations give rise to parsimonious representations of\nthe covariance per cluster (following Mc Nicholas and Murphy\n(2008) <DOI:10.1007/s11222-008-9056-0>). The model\nparameterization and number of factors is selected according to\nthe Bayesian Information Criterion. Identifiability issues\nrelated to label switching are dealt by post-processing the\nsimulated output with the Equivalence Classes Representatives\nalgorithm (Papastamoulis and Iliopoulos (2010)\n<DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016)\n<DOI:10.18637/jss.v069.c01>).",
  "License": "GPL-2",
  "URL": "https://github.com/mqbssppe/overfittingFABMix",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-01 10:31:24 UTC",
    "User": "root"
  },
  "Author": "Panagiotis Papastamoulis [aut, cre]\n(<https://orcid.org/0000-0001-9468-7613>)",
  "Repository": "https://mqbssppe.r-universe.dev",
  "Date/Publication": "2024-02-13 02:30:41 UTC",
  "RemoteUrl": "https://github.com/cran/fabMix",
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  "_created": "2026-06-01T10:31:24.000Z",
  "_published": "2026-06-01T10:45:30.097Z",
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  "_topics": [
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    "cpp",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
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      "date": "2020-02-19"
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      "version": "5.1",
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  "_exports": [
    "complete.log.likelihood",
    "complete.log.likelihood_q0",
    "complete.log.likelihood_q0_sameSigma",
    "complete.log.likelihood_Sj",
    "compute_A_B_G_D_and_simulate_mu_Lambda",
    "compute_A_B_G_D_and_simulate_mu_Lambda_CCU",
    "compute_A_B_G_D_and_simulate_mu_Lambda_CUU",
    "compute_A_B_G_D_and_simulate_mu_Lambda_q0",
    "compute_A_B_G_D_and_simulate_mu_Lambda_q0_sameSigma",
    "compute_A_B_G_D_and_simulate_mu_Lambda_Sj",
    "compute_sufficient_statistics",
    "compute_sufficient_statistics_given_mu",
    "compute_sufficient_statistics_q0",
    "CorMat_mcmc_summary",
    "CovMat_mcmc_summary",
    "dealWithLabelSwitching",
    "fabMix",
    "fabMix_CxC",
    "fabMix_CxU",
    "fabMix_missing_values",
    "fabMix_parallelModels",
    "fabMix_UxC",
    "fabMix_UxU",
    "getStuffForDIC",
    "log_dirichlet_pdf",
    "myDirichlet",
    "observed.log.likelihood0",
    "observed.log.likelihood0_q0_sameSigma",
    "observed.log.likelihood0_Sj",
    "observed.log.likelihood0_Sj_q0",
    "overfitting_q0",
    "overfitting_q0_sameSigma",
    "overfittingMFA",
    "overfittingMFA_CCC",
    "overfittingMFA_CCU",
    "overfittingMFA_CUC",
    "overfittingMFA_CUU",
    "overfittingMFA_missing_values",
    "overfittingMFA_Sj",
    "overfittingMFA_Sj_missing_values",
    "overfittingMFA_UCC",
    "overfittingMFA_UUC",
    "plot.fabMix.object",
    "print.fabMix.object",
    "readLambdaValues",
    "simData",
    "simData2",
    "summary.fabMix.object",
    "update_all_y",
    "update_all_y_Sj",
    "update_OmegaINV",
    "update_OmegaINV_Cxx",
    "update_SigmaINV_faster",
    "update_SigmaINV_faster_q0",
    "update_SigmaINV_faster_q0_sameSigma",
    "update_SigmaINV_faster_Sj",
    "update_SigmaINV_xCC",
    "update_SigmaINV_xUC",
    "update_z_b",
    "update_z_b_Sj",
    "update_z_q0",
    "update_z_q0_sameSigma",
    "update_z2",
    "update_z2_Sj",
    "update_z4",
    "update_z4_Sj"
  ],
  "_datasets": [
    {
      "name": "waveDataset1500",
      "title": "Wave dataset",
      "object": "waveDataset1500",
      "file": "waveDataset1500.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "class",
        "V1",
        "V2",
        "V3",
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        "V10",
        "V11",
        "V12",
        "V13",
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        "V18",
        "V19",
        "V20",
        "V21"
      ],
      "rows": 1500,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "fabMix-package",
      "title": "Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components",
      "topics": [
        "fabMix-package"
      ]
    },
    {
      "page": "complete.log.likelihood",
      "title": "Complete log-likelihood function for xCx models.",
      "topics": [
        "complete.log.likelihood"
      ]
    },
    {
      "page": "complete.log.likelihood_q0",
      "title": "Complete log-likelihood function for xUx models and q=0",
      "topics": [
        "complete.log.likelihood_q0"
      ]
    },
    {
      "page": "complete.log.likelihood_q0_sameSigma",
      "title": "Complete log-likelihood function for xCx models and q=0",
      "topics": [
        "complete.log.likelihood_q0_sameSigma"
      ]
    },
    {
      "page": "complete.log.likelihood_Sj",
      "title": "Complete log-likelihood function for xUx models.",
      "topics": [
        "complete.log.likelihood_Sj"
      ]
    },
    {
      "page": "compute_A_B_G_D_and_simulate_mu_Lambda",
      "title": "Computation and simulations",
      "topics": [
        "compute_A_B_G_D_and_simulate_mu_Lambda"
      ]
    },
    {
      "page": "compute_A_B_G_D_and_simulate_mu_Lambda_CCU",
      "title": "Computation and simulations for CCU",
      "topics": [
        "compute_A_B_G_D_and_simulate_mu_Lambda_CCU"
      ]
    },
    {
      "page": "compute_A_B_G_D_and_simulate_mu_Lambda_CUU",
      "title": "Computation and simulations for CUU",
      "topics": [
        "compute_A_B_G_D_and_simulate_mu_Lambda_CUU"
      ]
    },
    {
      "page": "compute_A_B_G_D_and_simulate_mu_Lambda_q0",
      "title": "Computation and simulations for q = 0.",
      "topics": [
        "compute_A_B_G_D_and_simulate_mu_Lambda_q0"
      ]
    },
    {
      "page": "compute_A_B_G_D_and_simulate_mu_Lambda_q0_sameSigma",
      "title": "Computation and simulations for q = 0.",
      "topics": [
        "compute_A_B_G_D_and_simulate_mu_Lambda_q0_sameSigma"
      ]
    },
    {
      "page": "compute_A_B_G_D_and_simulate_mu_Lambda_Sj",
      "title": "Computation and simulations",
      "topics": [
        "compute_A_B_G_D_and_simulate_mu_Lambda_Sj"
      ]
    },
    {
      "page": "compute_sufficient_statistics",
      "title": "Compute sufficient statistics",
      "topics": [
        "compute_sufficient_statistics"
      ]
    },
    {
      "page": "compute_sufficient_statistics_given_mu",
      "title": "Compute sufficient statistics given mu",
      "topics": [
        "compute_sufficient_statistics_given_mu"
      ]
    },
    {
      "page": "compute_sufficient_statistics_q0",
      "title": "Compute sufficient statistics for q = 0",
      "topics": [
        "compute_sufficient_statistics_q0"
      ]
    },
    {
      "page": "CorMat_mcmc_summary",
      "title": "Compute quantiles for the correlation matrix.",
      "topics": [
        "CorMat_mcmc_summary"
      ]
    },
    {
      "page": "CovMat_mcmc_summary",
      "title": "Compute quantiles for the covariance matrix.",
      "topics": [
        "CovMat_mcmc_summary"
      ]
    },
    {
      "page": "dealWithLabelSwitching",
      "title": "Apply label switching algorithms",
      "topics": [
        "dealWithLabelSwitching"
      ]
    },
    {
      "page": "fabMix",
      "title": "Main function",
      "topics": [
        "fabMix"
      ]
    },
    {
      "page": "fabMix_CxC",
      "title": "Function to estimate the 'CUC' and 'CCC' models",
      "topics": [
        "fabMix_CxC"
      ]
    },
    {
      "page": "fabMix_CxU",
      "title": "Function to estimate the 'CCU' and 'CUU' models",
      "topics": [
        "fabMix_CxU"
      ]
    },
    {
      "page": "fabMix_missing_values",
      "title": "Function to estimate the UUU or UCU models in case of missing values",
      "topics": [
        "fabMix_missing_values"
      ]
    },
    {
      "page": "fabMix_parallelModels",
      "title": "Function for model-level parallelization",
      "topics": [
        "fabMix_parallelModels"
      ]
    },
    {
      "page": "fabMix_UxC",
      "title": "Function to estimate the 'UUC' and 'UCC' models",
      "topics": [
        "fabMix_UxC"
      ]
    },
    {
      "page": "fabMix_UxU",
      "title": "Function to estimate the 'UUU' and 'UCU' model",
      "topics": [
        "fabMix_UxU"
      ]
    },
    {
      "page": "getStuffForDIC",
      "title": "Compute information criteria",
      "topics": [
        "getStuffForDIC"
      ]
    },
    {
      "page": "log_dirichlet_pdf",
      "title": "Log-density function of the Dirichlet distribution",
      "topics": [
        "log_dirichlet_pdf"
      ]
    },
    {
      "page": "myDirichlet",
      "title": "Simulate from the Dirichlet distribution",
      "topics": [
        "myDirichlet"
      ]
    },
    {
      "page": "observed.log.likelihood0",
      "title": "Log-likelihood of the mixture model",
      "topics": [
        "observed.log.likelihood0"
      ]
    },
    {
      "page": "observed.log.likelihood0_q0_sameSigma",
      "title": "Log-likelihood of the mixture model for q=0 and same variance of errors",
      "topics": [
        "observed.log.likelihood0_q0_sameSigma"
      ]
    },
    {
      "page": "observed.log.likelihood0_Sj",
      "title": "Log-likelihood of the mixture model",
      "topics": [
        "observed.log.likelihood0_Sj"
      ]
    },
    {
      "page": "observed.log.likelihood0_Sj_q0",
      "title": "Log-likelihood of the mixture model for q=0",
      "topics": [
        "observed.log.likelihood0_Sj_q0"
      ]
    },
    {
      "page": "overfitting_q0",
      "title": "MCMC sampler for q=0",
      "topics": [
        "overfitting_q0"
      ]
    },
    {
      "page": "overfitting_q0_sameSigma",
      "title": "MCMC sampler for q=0 and same error variance parameterization",
      "topics": [
        "overfitting_q0_sameSigma"
      ]
    },
    {
      "page": "overfittingMFA",
      "title": "Basic MCMC sampler for the 'UCU' model",
      "topics": [
        "overfittingMFA"
      ]
    },
    {
      "page": "overfittingMFA_CCC",
      "title": "Basic MCMC sampler for the 'CCC' model",
      "topics": [
        "overfittingMFA_CCC"
      ]
    },
    {
      "page": "overfittingMFA_CCU",
      "title": "Basic MCMC sampler for the 'CCU' model",
      "topics": [
        "overfittingMFA_CCU"
      ]
    },
    {
      "page": "overfittingMFA_CUC",
      "title": "Basic MCMC sampler for the 'CUC' model",
      "topics": [
        "overfittingMFA_CUC"
      ]
    },
    {
      "page": "overfittingMFA_CUU",
      "title": "Basic MCMC sampler for the 'CUU' model",
      "topics": [
        "overfittingMFA_CUU"
      ]
    },
    {
      "page": "overfittingMFA_missing_values",
      "title": "Basic MCMC sampler for the case of missing data",
      "topics": [
        "overfittingMFA_missing_values"
      ]
    },
    {
      "page": "overfittingMFA_Sj",
      "title": "Basic MCMC sampler for the 'UUU' model",
      "topics": [
        "overfittingMFA_Sj"
      ]
    },
    {
      "page": "overfittingMFA_Sj_missing_values",
      "title": "Basic MCMC sampler for the case of missing data and different error variance",
      "topics": [
        "overfittingMFA_Sj_missing_values"
      ]
    },
    {
      "page": "overfittingMFA_UCC",
      "title": "Basic MCMC sampler for the 'UCC' model",
      "topics": [
        "overfittingMFA_UCC"
      ]
    },
    {
      "page": "overfittingMFA_UUC",
      "title": "Basic MCMC sampler for the 'UUC' model",
      "topics": [
        "overfittingMFA_UUC"
      ]
    },
    {
      "page": "plot.fabMix.object",
      "title": "Plot function",
      "topics": [
        "plot.fabMix.object"
      ]
    },
    {
      "page": "print.fabMix.object",
      "title": "Print function",
      "topics": [
        "print.fabMix.object"
      ]
    },
    {
      "page": "readLambdaValues",
      "title": "Read Lambda values.",
      "topics": [
        "readLambdaValues"
      ]
    },
    {
      "page": "simData",
      "title": "Synthetic data generator",
      "topics": [
        "simData"
      ]
    },
    {
      "page": "simData2",
      "title": "Synthetic data generator 2",
      "topics": [
        "simData2"
      ]
    },
    {
      "page": "summary.fabMix.object",
      "title": "Summary method",
      "topics": [
        "summary.fabMix.object"
      ]
    },
    {
      "page": "update_all_y",
      "title": "Gibbs sampling for y in 'xCx' model",
      "topics": [
        "update_all_y"
      ]
    },
    {
      "page": "update_all_y_Sj",
      "title": "Gibbs sampling for y in 'xUx' model",
      "topics": [
        "update_all_y_Sj"
      ]
    },
    {
      "page": "update_OmegaINV",
      "title": "Gibbs sampling for Omega^{-1}",
      "topics": [
        "update_OmegaINV"
      ]
    },
    {
      "page": "update_OmegaINV_Cxx",
      "title": "Gibbs sampling for Omega^{-1} for Cxx model",
      "topics": [
        "update_OmegaINV_Cxx"
      ]
    },
    {
      "page": "update_SigmaINV_faster",
      "title": "Gibbs sampling for Sigma^{-1}",
      "topics": [
        "update_SigmaINV_faster"
      ]
    },
    {
      "page": "update_SigmaINV_faster_q0",
      "title": "Gibbs sampling for Sigma^{-1} per component for q=0",
      "topics": [
        "update_SigmaINV_faster_q0"
      ]
    },
    {
      "page": "update_SigmaINV_faster_q0_sameSigma",
      "title": "Gibbs sampling for Sigma^{-1} per component for q=0",
      "topics": [
        "update_SigmaINV_faster_q0_sameSigma"
      ]
    },
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