A series of test/training partitions are created using
`createDataPartition`

while `createResample`

creates one or more
bootstrap samples. `createFolds`

splits the data into `k`

groups
while `createTimeSlices`

creates cross-validation split for series data.
`groupKFold`

splits the data based on a grouping factor.

```
createDataPartition(
y,
times = 1,
p = 0.5,
list = TRUE,
groups = min(5, length(y))
)
```createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)

createMultiFolds(y, k = 10, times = 5)

createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE, skip = 0)

groupKFold(group, k = length(unique(group)))

createResample(y, times = 10, list = TRUE)

y

a vector of outcomes. For `createTimeSlices`

, these should be
in chronological order.

times

the number of partitions to create

p

the percentage of data that goes to training

list

logical - should the results be in a list (`TRUE`

) or a
matrix with the number of rows equal to `floor(p * length(y))`

and
`times`

columns.

groups

for numeric `y`

, the number of breaks in the quantiles
(see below)

k

an integer for the number of folds.

returnTrain

a logical. When true, the values returned are the sample
positions corresponding to the data used during training. This argument
only works in conjunction with `list = TRUE`

initialWindow

The initial number of consecutive values in each training set sample

horizon

the number of consecutive values in test set sample

fixedWindow

logical, if `FALSE`

, all training samples start at 1

skip

integer, how many (if any) resamples to skip to thin the total amount

group

a vector of groups whose length matches the number of rows in the overall data set.

A list or matrix of row position integers corresponding to the
training data. For `createTimeSlices`

subsamples are named by the end
index of each training subsample.

For bootstrap samples, simple random sampling is used.

For other data splitting, the random sampling is done within the levels of
`y`

when `y`

is a factor in an attempt to balance the class
distributions within the splits.

For numeric `y`

, the sample is split into groups sections based on
percentiles and sampling is done within these subgroups. For
`createDataPartition`

, the number of percentiles is set via the
`groups`

argument. For `createFolds`

and `createMultiFolds`

,
the number of groups is set dynamically based on the sample size and
`k`

. For smaller samples sizes, these two functions may not do
stratified splitting and, at most, will split the data into quartiles.

Also, for `createDataPartition`

, very small class sizes (<= 3) the
classes may not show up in both the training and test data

For multiple k-fold cross-validation, completely independent folds are
created. The names of the list objects will denote the fold membership
using the pattern "Foldi.Repj" meaning the ith section (of k) of the jth
cross-validation set (of `times`

). Note that this function calls
`createFolds`

with `list = TRUE`

and `returnTrain = TRUE`

.

Hyndman and Athanasopoulos (2013)) discuss rolling forecasting origin
techniques that move the training and test sets in time.
`createTimeSlices`

can create the indices for this type of splitting.

For Group k-fold cross-validation, the data are split such that no group
is contained in both the modeling and holdout sets. One or more group
could be left out, depending on the value of `k`

.

http://topepo.github.io/caret/data-splitting.html

Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. https://otexts.com/fpp2/

# NOT RUN { data(oil) createDataPartition(oilType, 2) x <- rgamma(50, 3, .5) inA <- createDataPartition(x, list = FALSE) plot(density(x[inA])) rug(x[inA]) points(density(x[-inA]), type = "l", col = 4) rug(x[-inA], col = 4) createResample(oilType, 2) createFolds(oilType, 10) createFolds(oilType, 5, FALSE) createFolds(rnorm(21)) createTimeSlices(1:9, 5, 1, fixedWindow = FALSE) createTimeSlices(1:9, 5, 1, fixedWindow = TRUE) createTimeSlices(1:9, 5, 3, fixedWindow = TRUE) createTimeSlices(1:9, 5, 3, fixedWindow = FALSE) createTimeSlices(1:15, 5, 3) createTimeSlices(1:15, 5, 3, skip = 2) createTimeSlices(1:15, 5, 3, skip = 3) set.seed(131) groups <- sort(sample(letters[1:4], size = 20, replace = TRUE)) table(groups) folds <- groupKFold(groups) lapply(folds, function(x, y) table(y[x]), y = groups) # }