Fix document() and check() warnings regarding [epiprocess::]epi_df

Workaround for epiprocess#493.

Author: brookslogan

R/arx_classifier.R

@@ -1,7 +1,7 @@
 #' Direct autoregressive classifier with covariates
 #'
 #' This is an autoregressive classification model for
-#' [epiprocess::epi_df] data. It does "direct" forecasting, meaning
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct" forecasting, meaning
 #' that it estimates a class at a particular target horizon.
 #'
 #' @inheritParams arx_forecaster

R/arx_forecaster.R

@@ -1,7 +1,7 @@
 #' Direct autoregressive forecaster with covariates
 #'
 #' This is an autoregressive forecasting model for
-#' [epiprocess::epi_df] data. It does "direct" forecasting, meaning
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct" forecasting, meaning
 #' that it estimates a model for a particular target horizon.
 #'
 #'

R/cdc_baseline_forecaster.R

@@ -1,7 +1,7 @@
 #' Predict the future with the most recent value
 #'
 #' This is a simple forecasting model for
-#' [epiprocess::epi_df] data. It uses the most recent observation as the
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It uses the most recent observation as the
 #' forecast for any future date, and produces intervals by shuffling the quantiles
 #' of the residuals of such a "flatline" forecast and incrementing these
 #' forward over all available training data.
@@ -12,7 +12,7 @@
 #' This forecaster is meant to produce exactly the CDC Baseline used for
 #' [COVID19ForecastHub](https://covid19forecasthub.org)
 #'
-#' @param epi_data An [`epiprocess::epi_df`]
+#' @param epi_data An [`epiprocess::epi_df`][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
 #' @param args_list A list of additional arguments as created by the
 #'   [cdc_baseline_args_list()] constructor function.

R/data.R

@@ -59,7 +59,7 @@
 
 #' Subset of Statistics Canada median employment income for postsecondary graduates
 #'
-#' @format An [epiprocess::epi_df] with 10193 rows and 8 variables:
+#' @format An [epiprocess::epi_df][epiprocess::as_epi_df] with 10193 rows and 8 variables:
 #' \describe{
 #'   \item{geo_value}{The province in Canada associated with each
 #'      row of measurements.}

R/epi_recipe.R

@@ -245,7 +245,7 @@ is_epi_recipe <- function(x) {
 #' @details
 #' `add_epi_recipe` has the same behaviour as
 #' [workflows::add_recipe()] but sets a different
-#' default blueprint to automatically handle [epiprocess::epi_df] data.
+#' default blueprint to automatically handle [epiprocess::epi_df][epiprocess::as_epi_df] data.
 #'
 #' @param x A `workflow` or `epi_workflow`
 #'

R/epi_workflow.R

@@ -119,7 +119,7 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #' - Call [parsnip::predict.model_fit()] for you using the underlying fit
 #'   parsnip model.
 #'
-#' - Ensure that the returned object is an [epiprocess::epi_df] where
+#' - Ensure that the returned object is an [epiprocess::epi_df][epiprocess::as_epi_df] where
 #'   possible. Specifically, the output will have `time_value` and
 #'   `geo_value` columns as well as the prediction.
 #'

R/flatline_forecaster.R

@@ -1,7 +1,7 @@
 #' Predict the future with today's value
 #'
 #' This is a simple forecasting model for
-#' [epiprocess::epi_df] data. It uses the most recent observation as the
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It uses the most recent observation as the
 #' forcast for any future date, and produces intervals based on the quantiles
 #' of the residuals of such a "flatline" forecast over all available training
 #' data.
@@ -13,7 +13,7 @@
 #' This forecaster is very similar to that used by the
 #' [COVID19ForecastHub](https://covid19forecasthub.org)
 #'
-#' @param epi_data An [epiprocess::epi_df]
+#' @param epi_data An [epiprocess::epi_df][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
 #' @param args_list A list of dditional arguments as created by the
 #'   [flatline_args_list()] constructor function.

R/get_test_data.R

@@ -1,7 +1,7 @@
 #' Get test data for prediction based on longest lag period
 #'
 #' Based on the longest lag period in the recipe,
-#' `get_test_data()` creates an [epi_df]
+#' `get_test_data()` creates an [epi_df][epiprocess::as_epi_df]
 #' with columns `geo_value`, `time_value`
 #' and other variables in the original dataset,
 #' which will be used to create features necessary to produce forecasts.