postprocessing -> post-processing

Author: dsweber2

NEWS.md

@@ -65,7 +65,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - training window step debugged
 - `min_train_window` argument removed from canned forecasters
 - add forecasters
-- implement postprocessing
+- implement post-processing
 - vignettes avaliable
 - arx_forecaster
 - pkgdown

R/epi_workflow.R

@@ -1,7 +1,7 @@
 #' Create an epi_workflow
 #'
 #' This is a container object that unifies preprocessing, fitting, prediction,
-#' and postprocessing for predictive modeling on epidemiological data. It extends
+#' and post-processing for predictive modeling on epidemiological data. It extends
 #' the functionality of a [workflows::workflow()] to handle the typical panel
 #' data structures found in this field. This extension is handled completely
 #' internally, and should be invisible to the user. For all intents and purposes,

R/frosting.R

@@ -246,10 +246,10 @@ new_frosting <- function() {
 }
 
 
-#' Create frosting for postprocessing predictions
+#' Create frosting for post-processing predictions
 #'
-#' This generates a postprocessing container (much like `recipes::recipe()`)
-#' to hold steps for postprocessing predictions.
+#' This generates a post-processing container (much like `recipes::recipe()`)
+#' to hold steps for post-processing predictions.
 #'
 #' The arguments are currently placeholders and must be NULL
 #'
@@ -260,7 +260,7 @@ new_frosting <- function() {
 #' @export
 #'
 #' @examples
-#' # Toy example to show that frosting can be created and added for postprocessing
+#' # Toy example to show that frosting can be created and added for post-processing
 #' f <- frosting()
 #' wf <- epi_workflow() %>% add_frosting(f)
 #'
@@ -322,9 +322,9 @@ extract_frosting.epi_workflow <- function(x, ...) {
   }
 }
 
-#' Apply postprocessing to a fitted workflow
+#' Apply post-processing to a fitted workflow
 #'
-#' This function is intended for internal use. It implements postprocessing
+#' This function is intended for internal use. It implements post-processing
 #' inside of the `predict()` method for a fitted workflow.
 #'
 #' @param workflow An object of class workflow
@@ -342,7 +342,7 @@ apply_frosting <- function(workflow, ...) {
 apply_frosting.default <- function(workflow, components, ...) {
   if (has_postprocessor(workflow)) {
     cli_abort(c(
-      "Postprocessing is only available for epi_workflows currently.",
+      "Post-processing is only available for epi_workflows currently.",
       i = "Can you use `epi_workflow()` instead of `workflow()`?"
     ))
   }

R/layer_add_forecast_date.R

@@ -1,4 +1,4 @@
-#' Postprocessing step to add the forecast date
+#' Post-processing step to add the forecast date
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param forecast_date The forecast date to add as a column to the `epi_df`.
@@ -7,17 +7,17 @@
 #'   values.  If there is a `step_adjust_latency` step present, it uses the
 #'   `forecast_date` as set in that function. Otherwise, it uses the maximum
 #'   `time_value` across the data used for pre-processing, fitting the model,
-#'   and postprocessing.
+#'   and post-processing.
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
 #'
 #' @details To use this function, either specify a forecast date or leave the
 #'  forecast date unspecifed here. In the latter case, the forecast date will
 #'  be set as the maximum time value from the data used in pre-processing,
-#'  fitting the model, and postprocessing. In any case, when the forecast date is
+#'  fitting the model, and post-processing. In any case, when the forecast date is
 #'  less than the maximum `as_of` value (from the data used pre-processing,
-#'  model fitting, and postprocessing), an appropriate warning will be thrown.
+#'  model fitting, and post-processing), an appropriate warning will be thrown.
 #'
 #' @export
 #' @examples

R/layer_add_target_date.R

@@ -1,12 +1,12 @@
-#' Postprocessing step to add the target date
+#' Post-processing step to add the target date
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param target_date The target date to add as a column to the `epi_df`. If
 #'   there's a forecast date specified upstream (either in a
 #'   `step_adjust_latency` or in a `layer_forecast_date`), then it is the
 #'   forecast date plus `ahead` (from `step_epi_ahead` in the `epi_recipe`).
 #'   Otherwise, it is the maximum `time_value` (from the data used in
-#'   pre-processing, fitting the model, and postprocessing) plus `ahead`, where
+#'   pre-processing, fitting the model, and post-processing) plus `ahead`, where
 #'   `ahead` has been specified in preprocessing. The user may override these by
 #'   specifying a target date of their own (of the form "yyyy-mm-dd").
 #' @param id a random id string

R/layer_point_from_distn.R

@@ -1,6 +1,6 @@
 #' Converts distributional forecasts to point forecasts
 #'
-#' This function adds a postprocessing layer to extract a point forecast from
+#' This function adds a post-processing layer to extract a point forecast from
 #' a distributional forecast. NOTE: With default arguments, this will remove
 #' information, so one should usually call this AFTER `layer_quantile_distn()`
 #' or set the `name` argument to something specific.

R/layer_predict.R

@@ -1,8 +1,8 @@
-#' Prediction layer for postprocessing
+#' Prediction layer for post-processing
 #'
 #' Implements prediction on a fitted `epi_workflow`. One may want different
 #' types of prediction, and to potentially apply this after some amount of
-#' postprocessing. This would typically be the first layer in a `frosting`
+#' post-processing. This would typically be the first layer in a `frosting`
 #' postprocessor.
 #'
 #' @seealso `parsnip::predict.model_fit()`

R/layer_threshold_preds.R

@@ -1,6 +1,6 @@
 #' Lower and upper thresholds for predicted values
 #'
-#' This postprocessing step is used to set prediction values that are
+#' This post-processing step is used to set prediction values that are
 #' smaller than the lower threshold or higher than the upper threshold equal
 #' to the threshold values.
 #'

R/layers.R

@@ -178,11 +178,11 @@ detect_layer.workflow <- function(x, name, ...) {
 
 #' Spread a layer of frosting on a fitted workflow
 #'
-#' Slathering frosting means to implement a postprocessing layer. When
-#' creating a new postprocessing layer, you must implement an S3 method
+#' Slathering frosting means to implement a post-processing layer. When
+#' creating a new post-processing layer, you must implement an S3 method
 #' for this function
 #'
-#' @param object a workflow with `frosting` postprocessing steps
+#' @param object a workflow with `frosting` post-processing steps
 #' @param components a list of components containing model information. These
 #'   will be updated and returned by the layer. These should be
 #'   * `mold` - the output of calling `hardhat::mold()` on the workflow. This

man/apply_frosting.Rd

@@ -88,8 +88,8 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
   expect_equal(ncol(b), 5L)
 })
 
-## Postprocessing
-test_that("Postprocessing workflow works and values correct", {
+## Post-processing
+test_that("Post-processing workflow works and values correct", {
   jhu <- epidatasets::cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
@@ -149,7 +149,7 @@ test_that("Postprocessing workflow works and values correct", {
   expect_equal(p$.pred_scaled, p$.pred * c(2, 3))
 })
 
-test_that("Postprocessing to get cases from case rate", {
+test_that("Post-processing to get cases from case rate", {
   jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)