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  "Title": "Semi-Supervised Gaussian Mixture Model with a Missing-Data\nMechanism",
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  "Authors@R": "c(person(\"Ziyang Lyu\", role = c(\"aut\", \"cre\"), email = \"ziyang.lyu@unsw.edu.au\"),person(\"Daniel Ahfock\", role = \"aut\"),person(\"Ryan Thompson\", role = \"aut\"),person(\"Geoffrey J. McLachlan\", role = \"aut\"))",
  "Description": "The algorithm of semi-supervised learning is based on\nfinite Gaussian mixture models and includes a mechanism for\nhandling missing data. It aims to fit a g-class Gaussian\nmixture model using maximum likelihood. The algorithm treats\nthe labels of unclassified features as missing data, building\non the framework introduced by Rubin (1976)\n<doi:10.2307/2335739> for missing data analysis. By taking into\naccount the dependencies in the missing pattern, the algorithm\nprovides more information for determining the optimal\nclassifier, as specified by Bayes' rule.",
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  "Author": "Ziyang Lyu [aut, cre], Daniel Ahfock [aut], Ryan Thompson\n[aut], Geoffrey J. McLachlan [aut]",
  "Maintainer": "Ziyang Lyu <ziyang.lyu@unsw.edu.au>",
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    "erate",
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    "predict",
    "pro2vec",
    "rlabel",
    "rmix",
    "summary",
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    "vec2pro"
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      "title": "Gastrointestinal dataset",
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        "tbl",
        "data.frame"
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        "class_agreement",
        "missinglabel_indicator",
        "ground truth"
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      "page": "bayesclassifier",
      "title": "Bayes' rule of allocation",
      "topics": [
        "bayesclassifier"
      ]
    },
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      "title": "Bootstrap Analysis for gmmsslm",
      "topics": [
        "bootstrap_gmmsslm"
      ]
    },
    {
      "page": "cov2vec",
      "title": "Transform a variance matrix into a vector",
      "topics": [
        "cov2vec"
      ]
    },
    {
      "page": "discriminant_beta",
      "title": "Discriminant function",
      "topics": [
        "discriminant_beta"
      ]
    },
    {
      "page": "erate",
      "title": "Error rate of the Bayes rule for a g-class Gaussian mixture model",
      "topics": [
        "erate"
      ]
    },
    {
      "page": "errorrate",
      "title": "Error rate of the Bayes rule for two-class Gaussian homoscedastic model",
      "topics": [
        "errorrate"
      ]
    },
    {
      "page": "gastro_data",
      "title": "Gastrointestinal dataset",
      "topics": [
        "gastro_data"
      ]
    },
    {
      "page": "get_clusterprobs",
      "title": "Posterior probability",
      "topics": [
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      ]
    },
    {
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      "title": "Shannon entropy",
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      ]
    },
    {
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      "title": "Fitting Gaussian mixture model to a complete classified dataset or an incomplete classified dataset with/without the missing-data mechanism.",
      "topics": [
        "gmmsslm"
      ]
    },
    {
      "page": "gmmsslmFit-class",
      "title": "gmmsslmFit Class",
      "topics": [
        "gmmsslmFit-class"
      ]
    },
    {
      "page": "initialvalue",
      "title": "Initial values for ECM",
      "topics": [
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      ]
    },
    {
      "page": "list2par",
      "title": "Transfer a list into a vector",
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    },
    {
      "page": "loglk_full",
      "title": "Full log-likelihood function",
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      "topics": [
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      ]
    },
    {
      "page": "loglk_miss",
      "title": "Log likelihood function formed on the basis of the missing-label indicator",
      "topics": [
        "loglk_miss"
      ]
    },
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      "page": "logsumexp",
      "title": "log summation of exponential function",
      "topics": [
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        "predict,gmmsslmFit-method"
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