add_changepoints_to_plot
                        Get layers to overlay significant changepoints
                        on prophet forecast plot.
add_country_holidays    Add in built-in holidays for the specified
                        country.
add_regressor           Add an additional regressor to be used for
                        fitting and predicting.
add_seasonality         Add a seasonal component with specified period,
                        number of Fourier components, and prior scale.
cross_validation        Cross-validation for time series.
dyplot.prophet          Plot the prophet forecast.
fit.prophet             Fit the prophet model.
generated_holidays      holidays table
make_future_dataframe   Make dataframe with future dates for
                        forecasting.
performance_metrics     Compute performance metrics from
                        cross-validation results.
plot.prophet            Plot the prophet forecast.
plot_cross_validation_metric
                        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.
plot_forecast_component
                        Plot a particular component of the forecast.
predict.prophet         Predict using the prophet model.
predictive_samples      Sample from the posterior predictive
                        distribution.
prophet                 Prophet forecaster.
prophet_plot_components
                        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.
regressor_coefficients
                        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.
rolling_median_by_h     Compute a rolling median of x, after first
                        aggregating by h
