blockCV                 blockCV: Spatial, Environmental, and Grouped
                        Cross-Validation
cv_block_size           Explore spatial block size
cv_buffer               Use buffer around records to separate train and
                        test folds (a.k.a. buffered/spatial
                        leave-one-out)
cv_cluster              Use environmental or spatial clustering to
                        separate train and test folds
cv_distance             Compare a cross-validation design to the
                        prediction domain via nearest-neighbour
                        distances
cv_group                Leave-group-out cross-validation using an
                        existing grouping factor
cv_knndm                Use the k-fold Nearest Neighbour Distance
                        Matching (kNNDM) to separate train and test
                        folds
cv_nndm                 Use the Nearest Neighbour Distance Matching
                        (NNDM) to separate train and test folds
cv_plot                 Visualising folds created by blockCV in ggplot
cv_similarity           Compute similarity measures to evaluate
                        possible extrapolation in testing folds
cv_spatial              Use spatial blocks to separate train and test
                        folds
cv_spatial_autocor      Measure spatial autocorrelation in spatial
                        response data or predictor raster files
cv_summary              Summarise the quality of a set of
                        cross-validation folds
