R/get_zoltar_predictions.R
    get_zoltar_predictions.RdSimply converts the predictions of forecasters submitting to the COVID Hub to the format of a predictions card, so it can be easily evaluated and compared.
get_zoltar_predictions( forecaster_names = NULL, forecast_dates = NULL, geo_values = "*", forecast_type = c("point", "quantile"), ahead = 1:4, incidence_period = c("epiweek", "day"), signal = c("confirmed_incidence_num", "deaths_incidence_num", "deaths_cumulative_num", "confirmed_admissions_covid_1d"), as_of = NULL )
| forecaster_names | Vector of strings indicating of the forecaster(s) (matching what it is called on the COVID Hub). | 
|---|---|
| forecast_dates | Vector of Date objects (or strings of the form
"YYYY-MM-DD") indicating dates on which forecasts will be made. If  | 
| geo_values | vector of character strings containing FIPS codes of counties, or lower case state abbreviations (or "us" for national). The default "*" fetches all available locations | 
| forecast_type | "quantile", "point" or both (the default) | 
| ahead | number of periods ahead for which the forecast is required. NULL will fetch all available aheads | 
| incidence_period | one of "epiweek" or "day". NULL will attempt to fetch both | 
| signal | this function supports only "confirmed_incidence_num", "deaths_incidence_num", "deaths_cumulative_num", and/or "confirmed_admissions_covid_1d". For other types, use one of the alternatives mentioned above | 
| as_of | only forecasts available as of this date will be retrieved. Default (NULL) is effectively as of today | 
Long data frame of forecasts with a class of predictions_cards.
The first 4 columns are the same as those returned by the forecaster. The
remainder specify the prediction task, 10 columns in total:
ahead, geo_value, quantile, value, forecaster, forecast_date,
data_source, signal, target_end_date, and incidence_period. Here
data_source and signal correspond to the response variable only.
Note: For greater flexibility, use zoltr::do_zoltar_query() or perhaps
covidHubUtils::load_forecasts().