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  "Title": "Inference for High-Dimensional Mixture Transition Distribution\nModels",
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  "Description": "Estimates parameters in Mixture Transition Distribution\n(MTD) models, a class of high-order Markov chains. The set of\nrelevant pasts (lags) is selected using either the Bayesian\nInformation Criterion or the Forward Stepwise and Cut\nalgorithms. Other model parameters (e.g. transition\nprobabilities and oscillations) can be estimated via maximum\nlikelihood estimation or the Expectation-Maximization\nalgorithm. Additionally, 'hdMTD' includes a perfect sampling\nalgorithm that generates samples of an MTD model from its\ninvariant distribution. For theory, see Ost & Takahashi (2023)\n<http://jmlr.org/papers/v24/22-0266.html>.",
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    "empirical_probs",
    "freqTab",
    "hdMTD",
    "hdMTD_BIC",
    "hdMTD_CUT",
    "hdMTD_FS",
    "hdMTD_FSC",
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    "Lambda",
    "lambdas",
    "MTDest",
    "MTDmodel",
    "oscillation",
    "p0",
    "perfectSample",
    "pj",
    "probs",
    "S",
    "states",
    "transitP"
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      "title": "Maximum temperatures in the city of Brasília, Brazil.",
      "object": "tempdata",
      "class": [
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        "tbl",
        "data.frame"
      ],
      "fields": [
        "DATE",
        "TIME",
        "MAXTEMP"
      ],
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      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as.MTD",
      "title": "Coerce an EM fit to an MTD model",
      "topics": [
        "as.MTD"
      ]
    },
    {
      "page": "countsTab",
      "title": "Counts sequences of length d+1 in a sample",
      "topics": [
        "countsTab"
      ]
    },
    {
      "page": "dTV_sample",
      "title": "The total variation distance between distributions",
      "topics": [
        "dTV_sample"
      ]
    },
    {
      "page": "empirical_probs",
      "title": "Estimated transition probabilities",
      "topics": [
        "empirical_probs"
      ]
    },
    {
      "page": "freqTab",
      "title": "A tibble containing sample sequence frequencies and estimated probabilities",
      "topics": [
        "freqTab"
      ]
    },
    {
      "page": "hdMTD",
      "title": "Inference in MTD models",
      "topics": [
        "hdMTD"
      ]
    },
    {
      "page": "hdMTD_BIC",
      "title": "The Bayesian Information Criterion (BIC) method for inference in MTD models",
      "topics": [
        "hdMTD_BIC"
      ]
    },
    {
      "page": "hdMTD_CUT",
      "title": "The CUT method for inference in MTD models",
      "topics": [
        "hdMTD_CUT"
      ]
    },
    {
      "page": "hdMTD_FS",
      "title": "The Forward Stepwise (FS) method for inference in MTD models",
      "topics": [
        "hdMTD_FS"
      ]
    },
    {
      "page": "hdMTD_FSC",
      "title": "Forward Stepwise and Cut method for inference in MTD models",
      "topics": [
        "hdMTD_FSC"
      ]
    },
    {
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      "title": "Methods for objects of class '\"hdMTD\"'",
      "topics": [
        "hdMTD-methods"
      ]
    },
    {
      "page": "MTD-accessors",
      "title": "Accessors for objects of classes '\"MTD\"', '\"MTDest\"', and/or '\"hdMTD\"'",
      "topics": [
        "lags",
        "Lambda",
        "lambdas",
        "MTD-accessors",
        "p0",
        "pj",
        "S",
        "states",
        "transitP"
      ]
    },
    {
      "page": "MTD-methods",
      "title": "Methods for objects of class '\"MTD\"'",
      "topics": [
        "MTD-methods"
      ]
    },
    {
      "page": "MTDest",
      "title": "EM estimation of MTD parameters",
      "topics": [
        "MTDest"
      ]
    },
    {
      "page": "MTDest-methods",
      "title": "Methods for objects of class '\"MTDest\"'",
      "topics": [
        "MTDest-methods"
      ]
    },
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        "MTDmodel"
      ]
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        "oscillation.default",
        "oscillation.MTD"
      ]
    },
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      ]
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      "title": "Plot method for MTD objects",
      "topics": [
        "plot.MTD"
      ]
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        "plot.MTDest"
      ]
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