#' Rolling Origin Forecast Resampling

#'

#' 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.

#' @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

#'  resampling continues while the assessment set size will always remain

#'  static.

#' `skip` enables the function to not use every data point in the resamples.

#'  When `skip = 0`, the resampling data sets will increment by one position.

#'  Suppose that the rows of a data set are consecutive days. Using `skip = 6`

#'  will make the analysis data set to operate on *weeks* instead of days. The

#'  assessment set size is not affected by this option.

#' @seealso

#' [sliding_window()], [sliding_index()], and [sliding_period()] for additional

#' time based resampling functions.

#' @inheritParams vfold_cv

#' @param initial The number of samples used for analysis/modeling in the

#'  initial resample.

#' @param assess The number of samples used for each assessment resample.

#' @param cumulative A logical. Should the analysis resample grow beyond the

#'  size specified by `initial` at each resample?.

#' @param skip A integer indicating how many (if any) _additional_ resamples

#'  to skip to thin the total amount of data points in the analysis resample.

#' See the example below.

#' @param lag A value to include a lag between the assessment

#'  and analysis set. This is useful if lagged predictors will be used

#'  during training and testing.

#' @export

#' @return An tibble with classes `rolling_origin`, `rset`, `tbl_df`, `tbl`,

#'  and `data.frame`. The results include a column for the data split objects

#'  and a column called `id` that has a character string with the resample

#'  identifier.

#' @examples

#' set.seed(1131)

#' ex_data <- data.frame(row = 1:20, some_var = rnorm(20))

#' dim(rolling_origin(ex_data))

#' dim(rolling_origin(ex_data, skip = 2))

#' dim(rolling_origin(ex_data, skip = 2, cumulative = FALSE))

#'

#' # You can also roll over calendar periods by first nesting by that period,

#' # which is especially useful for irregular series where a fixed window

#' # is not useful. This example slides over 5 years at a time.

#' library(dplyr)

#' library(tidyr)

#' data(drinks, package = "modeldata")

#'

#' drinks_annual <- drinks %>%

#'   mutate(year = as.POSIXlt(date)$year + 1900) %>%

#'   nest(-year)

#'

#' multi_year_roll <- rolling_origin(drinks_annual, cumulative = FALSE)

#'

#' analysis(multi_year_roll$splits[[1]])

#' assessment(multi_year_roll$splits[[1]])

#'

#' @export

rolling_origin <- function(data, initial = 5, assess = 1,

                           cumulative = TRUE, skip = 0, lag = 0, ...) {

  }

  }

  } else {

  }

}

#' @export

print.rolling_origin <- function(x, ...) {

}