These functions represent different cross-validation schemes that can be
used with origami. They should be used as options for the
`fold_fun`

argument to `make_folds`

, which will call the
requested function specify `n`

, based on its arguments, and pass any
remaining arguments (e.g. `V`

or `pvalidation`

) on.

```
folds_vfold(n, V = 10L)
folds_resubstitution(n)
folds_loo(n)
folds_montecarlo(n, V = 1000L, pvalidation = 0.2)
folds_bootstrap(n, V = 1000L)
folds_rolling_origin(n, first_window, validation_size, gap = 0L, batch = 1L)
folds_rolling_window(n, window_size, validation_size, gap = 0L, batch = 1L)
folds_rolling_origin_pooled(
n,
t,
id = NULL,
time = NULL,
first_window,
validation_size,
gap = 0L,
batch = 1L
)
folds_rolling_window_pooled(
n,
t,
id = NULL,
time = NULL,
window_size,
validation_size,
gap = 0L,
batch = 1L
)
folds_vfold_rolling_origin_pooled(
n,
t,
id = NULL,
time = NULL,
V = 10L,
first_window,
validation_size,
gap = 0L,
batch = 1L
)
folds_vfold_rolling_window_pooled(
n,
t,
id = NULL,
time = NULL,
V = 10L,
window_size,
validation_size,
gap = 0L,
batch = 1L
)
```

- n
An integer indicating the number of observations.

- V
An integer indicating the number of folds.

- pvalidation
A

`numeric`

indicating the proportion of observation to be placed in the validation fold.- first_window
An integer indicating the number of observations in the first training sample.

- validation_size
An integer indicating the number of points in the validation samples; should be equal to the largest forecast horizon.

- gap
An integer indicating the number of points not included in the training or validation samples. The default is zero.

- batch
An integer indicating increases in the number of time points added to the training set in each iteration of cross-validation. Applicable for larger time-series. The default is one.

- window_size
An integer indicating the number of observations in each training sample.

- t
An integer indicating the total amount of time to consider per time-series sample.

- id
An optional vector of unique identifiers corresponding to the time vector. These can be used to subset the time vector.

- time
An optional vector of integers of time points observed for each subject in the sample.

A list of `Fold`

s.

Other fold generation functions:
`fold_from_foldvec()`

,
`folds2foldvec()`

,
`make_folds()`

,
`make_repeated_folds()`