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blockCV (version 3.1-6)

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

Description

Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) .

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Install

install.packages('blockCV')

Monthly Downloads

2,178

Version

3.1-6

License

GPL (>= 3)

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Repository

https://github.com/rvalavi/blockCV

Maintainer

Roozbeh Valavi

Last Published

June 23rd, 2025

Functions in blockCV (3.1-6)

Explore spatial block size

spatialAutoRange

Measure spatial autocorrelation in the predictor raster files

Use distance (buffer) around records to separate train and test folds

Visualising folds created by blockCV in ggplot

Explore spatial block size

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

Use environmental or spatial clustering to separate train and test folds

cv_spatial_autocor

Measure spatial autocorrelation in spatial response data or predictor raster files

Use spatial blocks to separate train and test folds

Use buffer around records to separate train and test folds (a.k.a. buffered/spatial leave-one-out)

Use the Nearest Neighbour Distance Matching (NNDM) to separate train and test folds

Use environmental clustering to separate train and test folds

Explore the generated folds

Compute similarity measures to evaluate possible extrapolation in testing folds

Use spatial blocks to separate train and test folds