# Spatial and Environmental Blocking for Cross-Validation

## Documentation for package ‘blockCV’ version 4.0-0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

blockCV-package | blockCV: Spatial, Environmental, and Grouped Cross-Validation
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