Method-specific data preparation

Make multiple samples of data

`cross_fold`

: Make 'folds' samples of the data, so `all(rbind(folds)==row.names(data))=TRUE`

`random`

: Makes `iterations`

random samples of size `holdout * nrow(data)`

1 2 3 4 5 | ```
multisample.cross_fold(data, folds = 10, dependent,
preserve_distribution = FALSE)
multisample.random(data, holdout = 0.2, iterations = 10, dependent,
preserve_distribution = FALSE)
``` |

`data` |
Data to sample |

`folds` |
Number of folds to create |

`dependent` |
The dependent variable in the data. Used only if |

`preserve_distribution` |
Logical, only applicable if the dependent variable is a factor |

`holdout` |
The fraction of data to be used as holdout set |

`iterations` |
Number of iterations to make |

A list of numeric vectors of length 'folds'

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