# Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation

## Documentation for package ‘blockCV’ version 3.1-3

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

## Help Pages

blockCV | blockCV: Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
buffering | Use distance (buffer) around records to separate train and test folds
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_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
envBlock | Use environmental clustering to separate train and test folds
foldExplorer | Explore the generated folds
rangeExplorer | Explore spatial block size
spatialAutoRange | Measure spatial autocorrelation in the predictor raster files
spatialBlock | Use spatial blocks to separate train and test folds