rsample has functions to create variations of a data set that can be used to evaluate models or to estimate the sampling distribution of some statistic.
A resample is the result of a two-way split of a
data set. For example, when bootstrapping, one part of the
resample is a sample with replacement of the original data.
The other part of the split contains the instances that were
not contained in the bootstrap sample. The data structure
rsplit is used to store a single resample.
When the data are split in two, the portion that are used to estimate the model or calculate the statistic is called the analysis set here. In machine learning this is sometimes called the "training set" but this would be poorly named since it might conflict with any initial split of the original data.
Conversely, the other data in the split are called the assessment data. In bootstrapping, these data are often called the "out-of-bag" samples.
A collection of resamples is contained in an
rset object.
The main resampling functions are: vfold_cv(),
bootstraps(), mc_cv(),
rolling_origin(), and nested_cv().