blockCV: blockCV: Spatial, Environmental, and Grouped Cross-Validation

In blockCV: Spatial and Environmental Blocking for Cross-Validation

blockCV: Spatial, Environmental, and Grouped Cross-Validation

Description

Random assignment of spatially structured or grouped observations to training and testing folds can underestimate prediction error and lead to inappropriate model selection (see Valavi et al., 2019, and references therein). The blockCV package creates training and testing folds for k-fold, leave-group-out, and leave-one-out (LOO) cross-validation. Strategies include spatial blocks, spatial or environmental clustering, existing grouping factors, spatial buffers, and nearest neighbour distance matching. The package also provides tools to visualise and diagnose fold designs, compare train-test separation with the prediction domain, assess environmental novelty, and investigate spatial autocorrelation and candidate block sizes. It supports spatial modelling applications such as remote-sensing classification, soil mapping, and species distribution modelling, including presence-absence and presence-background data.

Author(s)

Roozbeh Valavi, Jane Elith, José Lahoz-Monfort, Ian Flint, and Gurutzeta Guillera-Arroita

References

Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225-232. doi:10.1111/2041-210X.13107.

See Also

vignette("tutorial_1", package = "blockCV") for examples of all fold-construction strategies, and vignette("tutorial_2", package = "blockCV") for fold assessment and design.

blockCV documentation built on Aug. 20, 2026, 5:10 p.m.