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  "Date": "2025-03-05",
  "Title": "Gaussian Parsimonious Clustering Models with Covariates and a\nNoise Component",
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  "Authors@R": "c(person(\"Keefe\", \"Murphy\", email = \"keefe.murphy@mu.ie\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-7709-3159\")),\nperson(\"Thomas Brendan\", \"Murphy\", email = \"brendan.murphy@ucd.ie\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5668-7046\")))",
  "Description": "Clustering via parsimonious Gaussian Mixtures of Experts\nusing the MoEClust models introduced by Murphy and Murphy\n(2020) <doi:10.1007/s11634-019-00373-8>. This package fits\nfinite Gaussian mixture models with a formula interface for\nsupplying gating and/or expert network covariates using a range\nof parsimonious covariance parameterisations from the GPCM\nfamily via the EM/CEM algorithm. Visualisation of the results\nof such models using generalised pairs plots and the inclusion\nof an additional noise component is also facilitated. A greedy\nforward stepwise search algorithm is provided for identifying\nthe optimal model in terms of the number of components, the\nGPCM covariance parameterisation, and the subsets of\ngating/expert network covariates.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "URL": "https://cran.r-project.org/package=MoEClust",
  "BugReports": "https://github.com/Keefe-Murphy/MoEClust/issues",
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  "Maintainer": "Keefe Murphy <keefe.murphy@mu.ie>",
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    "MoE_news",
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    "MoE_plotGate",
    "MoE_plotLogLik",
    "MoE_Similarity",
    "MoE_stepwise",
    "MoE_Uncertainty",
    "noise_vol",
    "quant_clust"
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      "tojson": true
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  "_help": [
    {
      "page": "MoEClust-package",
      "title": "MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component",
      "topics": [
        "MoEClust-package",
        "MoEClust"
      ]
    },
    {
      "page": "ais",
      "title": "Australian Institute of Sport data",
      "topics": [
        "ais"
      ]
    },
    {
      "page": "aitken",
      "title": "Aitken Acceleration",
      "topics": [
        "aitken"
      ]
    },
    {
      "page": "as.Mclust",
      "title": "Convert MoEClust objects to the Mclust class",
      "topics": [
        "as.Mclust",
        "as.Mclust.MoEClust"
      ]
    },
    {
      "page": "CO2data",
      "title": "GNP and CO2 Data Set",
      "topics": [
        "CO2data"
      ]
    },
    {
      "page": "drop_constants",
      "title": "Drop constant variables from a formula",
      "topics": [
        "drop_constants"
      ]
    },
    {
      "page": "drop_levels",
      "title": "Drop unused factor levels to predict from unseen data",
      "topics": [
        "drop_levels"
      ]
    },
    {
      "page": "expert_covar",
      "title": "Account for extra variability in covariance matrices with expert covariates",
      "topics": [
        "expert_covar"
      ]
    },
    {
      "page": "FARI",
      "title": "Compute the Frobenius (adjusted) Rand index",
      "topics": [
        "FARI"
      ]
    },
    {
      "page": "force_posiDiag",
      "title": "Force diagonal elements of a triangular matrix to be positive",
      "topics": [
        "force_posiDiag"
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    },
    {
      "page": "MoE_AvePP",
      "title": "Average posterior probabilities of a fitted MoEClust model",
      "topics": [
        "MoE_AvePP"
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    },
    {
      "page": "MoE_clust",
      "title": "MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component",
      "topics": [
        "MoE_clust",
        "print.MoEClust",
        "summary.MoEClust"
      ]
    },
    {
      "page": "MoE_compare",
      "title": "Choose the best MoEClust model",
      "topics": [
        "MoE_compare",
        "print.MoECompare"
      ]
    },
    {
      "page": "MoE_control",
      "title": "Set control values for use with MoEClust",
      "topics": [
        "MoE_control"
      ]
    },
    {
      "page": "MoE_crit",
      "title": "MoEClust BIC, ICL, and AIC Model-Selection Criteria",
      "topics": [
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    {
      "page": "MoE_cstep",
      "title": "C-step for MoEClust Models",
      "topics": [
        "MoE_cstep"
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    {
      "page": "MoE_dens",
      "title": "Density for MoEClust Mixture Models",
      "topics": [
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    {
      "page": "MoE_entropy",
      "title": "Entropy of a fitted MoEClust model",
      "topics": [
        "MoE_entropy"
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    },
    {
      "page": "MoE_estep",
      "title": "E-step for MoEClust Models",
      "topics": [
        "MoE_estep"
      ]
    },
    {
      "page": "MoE_gpairs",
      "title": "Generalised Pairs Plots for MoEClust Mixture Models",
      "topics": [
        "MoE_gpairs"
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    },
    {
      "page": "MoE_mahala",
      "title": "Mahalanobis Distance Outlier Detection for Multivariate Response",
      "topics": [
        "MoE_mahala"
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    {
      "page": "MoE_news",
      "title": "Show the NEWS file",
      "topics": [
        "MoE_news"
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    },
    {
      "page": "MoE_plotCrit",
      "title": "Model Selection Criteria Plot for MoEClust Mixture Models",
      "topics": [
        "MoE_plotCrit"
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    {
      "page": "MoE_plotGate",
      "title": "Plot MoEClust Gating Network",
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    {
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        "residuals.MoEClust"
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      "page": "quant_clust",
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