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  "Title": "Accuracy Statistic Estimation for Imperfect Gold Standards",
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  "Description": "Produce maximum likelihood estimates of common accuracy\nstatistics for multiple measurement methods when a gold\nstandard is not available. An R implementation of the\nexpectation maximization algorithms described in Zhou et al.\n(2011) <doi:10.1002/9780470906514> with additional functions\nfor creating simulated data and visualizing results. Supports\nbinary, ordinal, and continuous measurement methods.",
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    "generate_multimethod_data",
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    "plot_ML",
    "plot_ML_binary",
    "plot_ML_continuous",
    "plot_ML_ordinal",
    "pollinate_ML",
    "pollinate_ML_binary",
    "pollinate_ML_continuous",
    "pollinate_ML_ordinal",
    "setFreqs",
    "show",
    "unique_obs_summary"
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    {
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      "title": "Calculate AUC for single Se/Sp pair",
      "topics": [
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      ]
    },
    {
      "page": "boot_ML",
      "title": "Bootstrap ML accuracy statistic estimation for multi-method data",
      "topics": [
        "boot_ML"
      ]
    },
    {
      "page": "censor_data",
      "title": "Censor data randomly rowwise",
      "topics": [
        "censor_data"
      ]
    },
    {
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      "title": "Define the True disease state of a simulated sample",
      "topics": [
        "define_disease_state"
      ]
    },
    {
      "page": "dmvnorm",
      "title": "Multivariate Normal Densities",
      "topics": [
        "dmvnorm"
      ]
    },
    {
      "page": "estimate_ML",
      "title": "Estimate maximum likelihood accuracy statistics by expectation maximization",
      "topics": [
        "estimate_ML",
        "estimate_ML_binary",
        "estimate_ML_continuous",
        "estimate_ML_ordinal"
      ]
    },
    {
      "page": "generate_multimethod_data",
      "title": "Create data sets which simulate paired measurements of multiple methods",
      "topics": [
        "generate_multimethod_binary",
        "generate_multimethod_continuous",
        "generate_multimethod_data",
        "generate_multimethod_ordinal"
      ]
    },
    {
      "page": "getNames",
      "title": "Return names from MultiMethodMLEstimate object",
      "topics": [
        "getNames"
      ]
    },
    {
      "page": "getNames-MultiMethodMLEstimate-method",
      "title": "Return names from MultiMethodMLEstimate object",
      "topics": [
        "getNames,MultiMethodMLEstimate-method"
      ]
    },
    {
      "page": "getResults",
      "title": "Return result from MultiMethodMLEstimate object",
      "topics": [
        "getResults"
      ]
    },
    {
      "page": "getResults-MultiMethodMLEstimate-method",
      "title": "Return result from MultiMethodMLEstimate object",
      "topics": [
        "getResults,MultiMethodMLEstimate-method"
      ]
    },
    {
      "page": "MultiMethodMLEstimate-class",
      "title": "S4 object containing the results of multi-method ML accuracy estimates",
      "topics": [
        "MultiMethodMLEstimate-class"
      ]
    },
    {
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      "title": "Create unique names for a set of things",
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        "name_thing"
      ]
    },
    {
      "page": "new_boot_ML",
      "title": "Create new boot_ML class object",
      "topics": [
        "new_boot_ML"
      ]
    },
    {
      "page": "plot_ML",
      "title": "Create plots visualizing the ML estimation process and results.",
      "topics": [
        "plot_ML",
        "plot_ML_binary",
        "plot_ML_continuous",
        "plot_ML_ordinal"
      ]
    },
    {
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      "title": "Create plots from a MultiMethodMLEstimate object",
      "topics": [
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    },
    {
      "page": "plot.boot_ML",
      "title": "Plot univariate distributions of bootstrapped ML estimates",
      "topics": [
        "plot.boot_ML"
      ]
    },
    {
      "page": "pollinate_ML",
      "title": "Generate seed values for EM algorithm",
      "topics": [
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        "pollinate_ML_binary",
        "pollinate_ML_continuous",
        "pollinate_ML_ordinal"
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    },
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        "random_start_binary",
        "random_start_ordinal"
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      ]
    },
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