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      "version": "0.2.1",
      "date": "2017-11-08"
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    {
      "version": "0.3",
      "date": "2018-06-02"
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    {
      "version": "0.3.0.1",
      "date": "2018-06-15"
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    {
      "version": "0.4",
      "date": "2018-12-21"
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    {
      "version": "0.5",
      "date": "2019-05-15"
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  "_exports": [
    "add_changepoints_to_plot",
    "add_country_holidays",
    "add_regressor",
    "add_seasonality",
    "cross_validation",
    "dyplot.prophet",
    "fit.prophet",
    "generated_holidays",
    "make_future_dataframe",
    "performance_metrics",
    "plot_cross_validation_metric",
    "plot_forecast_component",
    "predictive_samples",
    "prophet",
    "prophet_plot_components",
    "regressor_coefficients"
  ],
  "_help": [
    {
      "page": "add_changepoints_to_plot",
      "title": "Get layers to overlay significant changepoints on prophet forecast plot.",
      "topics": [
        "add_changepoints_to_plot"
      ]
    },
    {
      "page": "add_country_holidays",
      "title": "Add in built-in holidays for the specified country.",
      "topics": [
        "add_country_holidays"
      ]
    },
    {
      "page": "add_regressor",
      "title": "Add an additional regressor to be used for fitting and predicting.",
      "topics": [
        "add_regressor"
      ]
    },
    {
      "page": "add_seasonality",
      "title": "Add a seasonal component with specified period, number of Fourier components, and prior scale.",
      "topics": [
        "add_seasonality"
      ]
    },
    {
      "page": "cross_validation",
      "title": "Cross-validation for time series.",
      "topics": [
        "cross_validation"
      ]
    },
    {
      "page": "dyplot.prophet",
      "title": "Plot the prophet forecast.",
      "topics": [
        "dyplot.prophet"
      ]
    },
    {
      "page": "fit.prophet",
      "title": "Fit the prophet model.",
      "topics": [
        "fit.prophet"
      ]
    },
    {
      "page": "generated_holidays",
      "title": "Generated table of holiday dates at the country level from 1995 to 2045",
      "topics": [
        "generated_holidays"
      ]
    },
    {
      "page": "make_future_dataframe",
      "title": "Make dataframe with future dates for forecasting.",
      "topics": [
        "make_future_dataframe"
      ]
    },
    {
      "page": "performance_metrics",
      "title": "Compute performance metrics from cross-validation results.",
      "topics": [
        "performance_metrics"
      ]
    },
    {
      "page": "plot_cross_validation_metric",
      "title": "Plot a performance metric vs. forecast horizon from cross validation. Cross validation produces a collection of out-of-sample model predictions that can be compared to actual values, at a range of different horizons (distance from the cutoff). This computes a specified performance metric for each prediction, and aggregated over a rolling window with horizon.",
      "topics": [
        "plot_cross_validation_metric"
      ]
    },
    {
      "page": "plot_forecast_component",
      "title": "Plot a particular component of the forecast.",
      "topics": [
        "plot_forecast_component"
      ]
    },
    {
      "page": "plot.prophet",
      "title": "Plot the prophet forecast.",
      "topics": [
        "plot.prophet"
      ]
    },
    {
      "page": "predict.prophet",
      "title": "Predict using the prophet model.",
      "topics": [
        "predict.prophet"
      ]
    },
    {
      "page": "predictive_samples",
      "title": "Sample from the posterior predictive distribution.",
      "topics": [
        "predictive_samples"
      ]
    },
    {
      "page": "prophet",
      "title": "Prophet forecaster.",
      "topics": [
        "prophet"
      ]
    },
    {
      "page": "prophet_plot_components",
      "title": "Plot the components of a prophet forecast. Prints a ggplot2 with whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors.",
      "topics": [
        "prophet_plot_components"
      ]
    },
    {
      "page": "regressor_coefficients",
      "title": "Summarise the coefficients of the extra regressors used in the model. For additive regressors, the coefficient represents the incremental impact on 'y' of a unit increase in the regressor. For multiplicative regressors, the incremental impact is equal to 'trend(t)' multiplied by the coefficient.",
      "topics": [
        "regressor_coefficients"
      ]
    },
    {
      "page": "rolling_median_by_h",
      "title": "Compute a rolling median of x, after first aggregating by h",
      "topics": [
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      ]
    }
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