Nothing

```
# ----------------------
# Author: Andreas Alfons
# KU Leuven
# ----------------------
## split data into blocks for cross-validation as in package 'lars'
#' Cross-validation folds
#'
#' Split \eqn{n} observations into \eqn{K} groups to be used for (repeated)
#' \eqn{K}-fold cross-validation. \eqn{K} should thereby be chosen such that
#' all groups are of approximately equal size.
#'
#' @aliases print.cvFolds
#'
#' @param n an integer giving the number of observations to be split into
#' groups.
#' @param K an integer giving the number of groups into which the observations
#' should be split (the default is five). Setting \code{K} equal to \code{n}
#' yields leave-one-out cross-validation.
#' @param R an integer giving the number of replications for repeated
#' \eqn{K}-fold cross-validation. This is ignored for for leave-one-out
#' cross-validation and other non-random splits of the data.
#' @param type a character string specifying the type of folds to be
#' generated. Possible values are \code{"random"} (the default),
#' \code{"consecutive"} or \code{"interleaved"}.
#'
#' @returnClass cvFolds
#' @returnItem n an integer giving the number of observations.
#' @returnItem K an integer giving the number of folds.
#' @returnItem R an integer giving the number of replications.
#' @returnItem subsets an integer matrix in which each column contains a
#' permutation of the indices.
#' @returnItem which an integer vector giving the fold for each permuted
#' observation.
#'
#' @author Andreas Alfons
#'
#' @seealso \code{\link{cvFit}}, \code{\link{cvSelect}}, \code{\link{cvTuning}}
#'
#' @examples
#' set.seed(1234) # set seed for reproducibility
#' cvFolds(20, K = 5, type = "random")
#' cvFolds(20, K = 5, type = "consecutive")
#' cvFolds(20, K = 5, type = "interleaved")
#' cvFolds(20, K = 5, R = 10)
#'
#' @keywords utilities
#'
#' @export
cvFolds <- function(n, K = 5, R = 1,
type = c("random", "consecutive", "interleaved")) {
# check arguments
n <- round(rep(n, length.out=1))
if(!isTRUE(n > 0)) stop("'n' must be positive")
K <- round(rep(K, length.out=1))
if(!isTRUE((K > 1) && K <= n)) stop("'K' outside allowable range")
type <- if(K == n) "leave-one-out" else match.arg(type)
# obtain CV folds
if(type == "random") {
# random K-fold splits with R replications
R <- round(rep(R, length.out=1))
if(!isTRUE(R > 0)) R <- 1
subsets <- replicate(R, sample(n))
} else {
# leave-one-out CV or non-random splits, replication not meaningful
R <- 1
subsets <- as.matrix(seq_len(n))
}
which <- rep(seq_len(K), length.out=n)
if(type == "consecutive") which <- rep.int(seq_len(K), tabulate(which))
# construct and return object
folds <- list(n=n, K=K, R=R, subsets=subsets, which=which)
class(folds) <- "cvFolds"
folds
}
# retrieve CV folds for r-th replication
getSubsetList <- function(x, ...) UseMethod("getSubsetList")
getSubsetList.cvFolds <- function(x, r = 1, ...) split(x$subsets[, r], x$which)
```

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