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blockCV

Spatial and Environmental Blocking for K-Fold Cross-Validation

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 to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.

Functions (7)

blockCV

blockCV: A package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution modelling.

buffering

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

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

blockCV

Spatial and Environmental Blocking for K-Fold Cross-Validation

v2.1.1
GPL-3
Authors
Roozbeh Valavi [aut, cre], Jane Elith [aut], José Lahoz-Monfort [aut], Gurutzeta Guillera-Arroita [aut]
Initial release
2020-02-16

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