Mark rolling_origin() as superseded

NEWS.md

@@ -6,6 +6,8 @@
 
 * Fixed example for `nested_cv()` (@seb09, #520).
 
+* `rolling_origin()` is now superseded by `sliding_window()`, `sliding_index()`, and `sliding_period()` which provide more flexibility and control (@nmercadeb, #524).
+
 * Removed trailing space in printing of `mc_cv()` objects (@ccani007, #464).
 
 * Improved documentation for `initial_split()` and friends (@laurabrianna, #519).

R/rolling_origin.R

@@ -1,9 +1,18 @@
 #' Rolling Origin Forecast Resampling
 #'
+#' @description
+#' `r lifecycle::badge("superseded")`
+#'
 #' This resampling method is useful when the data set has a strong time
 #'  component. The resamples are not random and contain data points that are
 #'  consecutive values. The function assumes that the original data set are
 #'  sorted in time order.
+#' 
+#' This function is superseded by [sliding_window()], [sliding_index()], and 
+#' [sliding_period()] which provide more flexibility and control. Superseded
+#' functions will not go away, but active development will be focused on the new
+#' functions. 
+#' 
 #' @details The main options, `initial` and `assess`, control the number of
 #'  data points from the original data that are in the analysis and assessment
 #'  set, respectively. When `cumulative = TRUE`, the analysis set will grow as
@@ -59,6 +68,13 @@
 #' @export
 rolling_origin <- function(data, initial = 5, assess = 1,
                            cumulative = TRUE, skip = 0, lag = 0, ...) {
+
+  lifecycle::signal_stage(
+    stage = "superseded", 
+    what = "rolling_origin()",
+    with = I("`sliding_window()`, `sliding_index()` and `sliding_period()`")
+  )
+
   check_dots_empty()
 
   n <- nrow(data)

vignettes/Common_Patterns.Rmd

@@ -223,11 +223,13 @@ sliding_period(Chicago, date, "year") %>%
   head(2)
 ```
 
-All of these functions produce analysis sets of the same size, with the start and end of the analysis set "sliding" down your data frame. If you'd rather have your analysis set get progressively larger, so that you're predicting new data based upon a growing set of older observations, you can use the `rolling_origin()` function:
+All of these functions produce analysis sets of the same size, with the start and end of the analysis set "sliding" down your data frame. If you'd rather have your analysis set get progressively larger, so that you're predicting new data based upon a growing set of older observations, you can use the `sliding_window()` function with `lookback = -Inf`:
 
 ```{r}
-rolling_origin(Chicago) %>%
+sliding_window(Chicago, lookback = Inf) %>%
   head(2)
 ```
 
+This is commonly referred to as "evaluation on a rolling forecasting origin", or more colloquially, "rolling origin cross-validation".
+
 Note that all of these time-based resampling functions are deterministic: unlike the rest of the package, running these functions repeatedly under different random seeds will always return the same results.