Spatial and Environmental Blocking for Cross-Validation

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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