integer, specify $k$ for $k$-fold cross-validation.

integer, number of bootstrap replications.

logical, if TRUE, cross-validation is performed using stratified sampling (for classification problems).

logical, if TRUE, cross-validation is performed using a random ordering of the data.

logical, indicates whether the prediction for each observation should be returned or not (classification and regression only). For a bootstrap based estimator a matrix of size 'number of observations' times nboot is returned with predicted values of the ith out-of-bootstrap sample in column i and 'NA's for those observations not included in the ith out-of-bootstrap sample.

logical, indicates a list of all models should be returned. For cross-validation only.

list of numeric vectors, indicating which observations are included in each bootstrap or cross-validation sample, respectively.