get_zoltar_predictions: Get predictions from a COVID forecaster in Zoltar
In dshemetov/evalcast-mirror: Tools For Evaluating COVID Forecasters

Description Usage Arguments Details Value See Also

Simply 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"),
  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 `NULL`, the default, then all currently available forecast dates from the given forecaster in the COVID Hub will be used.
`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", and/or "deaths_cumulative_num" (those currently) forecast by the COVIDhub-ensemble). 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

Note: For greater flexibility, use zoltr::do_zoltar_query() or perhaps covidHubUtils::load_forecasts().

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.