Package: blockCV 3.1-4

blockCV: Spatial and Environmental Blocking for K-Fold and LOO 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 and point samples 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>.

Authors:Roozbeh Valavi [aut, cre], Jane Elith [aut], José Lahoz-Monfort [aut], Ian Flint [aut], Gurutzeta Guillera-Arroita [aut]

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Peer review:

Bug tracker:https://github.com/rvalavi/blockcv/issues

On CRAN:

cross-validationspatialspatial-cross-validationspatial-modellingspecies-distribution-modelling

13 exports 106 stars 4.70 score 13 dependencies 4 dependents 5 mentions 267 scripts 1.2k downloads

Last updated 2 months agofrom:582c77a6d3. Checks:OK: 1 WARNING: 8. Indexed: yes.

Exports:bufferingcv_block_sizecv_buffercv_clustercv_nndmcv_plotcv_similaritycv_spatialcv_spatial_autocorenvBlockrangeExplorerspatialAutoRangespatialBlock

Dependencies:classclassIntDBIe1071KernSmoothmagrittrMASSproxyRcpps2sfunitswk