{
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  "Package": "VisitorCounts",
  "Type": "Package",
  "Title": "Modeling and Forecasting Visitor Counts Using Social Media",
  "Version": "2.0.4",
  "Date": "2026-05-26",
  "Authors@R": "c(\nperson(\"Robert\", \"Bowen\", email = \"robertbowen.bham@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Russell\", \"Goebel\", email = \"rgoebel@bu.edu\", role = \"aut\"),\nperson(\"Beth Ann\", \"Brackett\", email = \"eliannkb@gmail.com\", role = \"ctb\"),\nperson(\"Kimihiro\", \"Noguchi\", email = \"Kimihiro.noguchi@wwu.edu\", role = \"aut\"),\nperson(\"Dylan\", \"Way\", email = \"wayd2@wwu.edu\", role=\"aut\")\n)",
  "Author": "Robert Bowen [aut, cre], Russell Goebel [aut], Beth Ann\nBrackett [ctb], Kimihiro Noguchi [aut], Dylan Way [aut]",
  "Maintainer": "Robert Bowen <robertbowen.bham@gmail.com>",
  "Description": "Performs modeling and forecasting of park visitor counts\nusing social media data and (partial) on-site visitor counts.\nSpecifically, the model is built based on an automatic\ndecomposition of the trend and seasonal components of the\nsocial media-based park visitor counts, from which short-term\nforecasts of the visitor counts and percent changes in the\nvisitor counts can be made. A reference for the underlying\nmodel that 'VisitorCounts' uses can be found at Russell Goebel,\nAustin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro\nNoguchi (2023) <doi:10.1002/for.2965> .",
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    "new_visitation_forecast",
    "new_visitation_forecast_ensemble",
    "new_visitation_model",
    "prediction_warning",
    "trim_training_data",
    "visitation_model",
    "yearsToMonths"
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      "title": "Popularity of Flickr, in User-Days",
      "object": "flickr_userdays",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "title": "National Forest Visitation Photo-User-Days Data.",
      "object": "forest_visitation",
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      ],
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        "forest",
        "nfs",
        "pud"
      ],
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      "table": true,
      "tojson": true
    },
    {
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      "title": "National Park Visitation Counts and Associated Photo-User-Days Data.",
      "object": "park_visitation",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
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        "park",
        "pud",
        "nps"
      ],
      "rows": 3120,
      "table": true,
      "tojson": true
    }
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    {
      "page": "auto_decompose",
      "title": "Automatic Decomposition Function",
      "topics": [
        "auto_decompose"
      ]
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    {
      "page": "check_arguments",
      "title": "Check Arguments",
      "topics": [
        "check_arguments"
      ]
    },
    {
      "page": "convert_ts_forecast_to_df",
      "title": "convert_ts_forecast_to_df",
      "topics": [
        "convert_ts_forecast_to_df"
      ]
    },
    {
      "page": "decompose_proxy",
      "title": "Decompose Popularity Proxy",
      "topics": [
        "decompose_proxy"
      ]
    },
    {
      "page": "estimate_lag",
      "title": "Estimate Lag Function",
      "topics": [
        "estimate_lag"
      ]
    },
    {
      "page": "estimate_parameters",
      "title": "Estimate Parameters for Visitation Model",
      "topics": [
        "estimate_parameters"
      ]
    },
    {
      "page": "fit_model",
      "title": "Fit Model",
      "topics": [
        "fit_model"
      ]
    },
    {
      "page": "flickr_userdays",
      "title": "Popularity of Flickr, in User-Days",
      "topics": [
        "flickr_userdays"
      ]
    },
    {
      "page": "forest_visitation",
      "title": "National Forest Visitation Photo-User-Days Data.",
      "topics": [
        "forest_visitation"
      ]
    },
    {
      "page": "generate_proxy_trend_forecasts",
      "title": "Generate Proxy Trend Forecasts",
      "topics": [
        "generate_proxy_trend_forecasts"
      ]
    },
    {
      "page": "ggplot.decomposition",
      "title": "Decomposition ggplot Method",
      "topics": [
        "ggplot.decomposition"
      ]
    },
    {
      "page": "ggplot.visitation_forecast",
      "title": "visitation_forecast ggPlot Method",
      "topics": [
        "ggplot.visitation_forecast"
      ]
    },
    {
      "page": "ggplot.visitation_forecast_ensemble",
      "title": "visitation_model visitation_forecast_ensemble ggplot Methods",
      "topics": [
        "ggplot.visitation_forecast_ensemble"
      ]
    },
    {
      "page": "ggplot.visitation_model",
      "title": "visitation_model ggplot2 method",
      "topics": [
        "ggplot.visitation_model"
      ]
    },
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      "title": "Imputation",
      "topics": [
        "imputation"
      ]
    },
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      "title": "labeled_visitation_forecast Class",
      "topics": [
        "label_visitation_forecast"
      ]
    },
    {
      "page": "new_decomposition",
      "title": "\"decomposition\" Constructor Function",
      "topics": [
        "new_decomposition"
      ]
    },
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      "topics": [
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      "topics": [
        "new_visitation_forecast_ensemble"
      ]
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      "title": "\"visitation_model\" Constructor Function",
      "topics": [
        "new_visitation_model"
      ]
    },
    {
      "page": "park_visitation",
      "title": "National Park Visitation Counts and Associated Photo-User-Days Data.",
      "topics": [
        "park_visitation"
      ]
    },
    {
      "page": "plot.decomposition",
      "title": "Decomposition Plot Methods",
      "topics": [
        "plot.decomposition"
      ]
    },
    {
      "page": "plot.visitation_forecast",
      "title": "visitation_forecast Plot Method",
      "topics": [
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      ]
    },
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      "title": "visitation_model Plot Methods",
      "topics": [
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      "title": "Predict Decomposition",
      "topics": [
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    {
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      "title": "Predict Visitation Model",
      "topics": [
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      ]
    },
    {
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      "title": "Notify User prediction warning on constant is 0",
      "topics": [
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      "title": "Decomposition Summary Method",
      "topics": [
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    },
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      "title": "visitation_forecast Summary Method",
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      "title": "Converting Annual Counts into Monthly Counts",
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        "Sample Datasets: park_visitation and flickr_userdays",
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