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


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Documentation for package ‘blockCV’ version 3.1-3

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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