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>.
Version: 3.1-6 Depends: R (≥ 3.5.0) Imports: sf (≥ 1.0), Rcpp (≥ 1.0.2) LinkingTo: Rcpp Suggests: terra (≥ 1.6-41), ggplot2 (≥ 3.3.6), cowplot, automap (≥ 1.0-16), shiny (≥ 1.7), tmap (≥ 2.0), biomod2, gstat, methods, knitr, rmarkdown, testthat (≥ 3.0.0), covr Published: 2025-06-23 DOI: 10.32614/CRAN.package.blockCV Author: Roozbeh Valavi [aut, cre], Jane Elith [aut], José Lahoz-Monfort [aut], Ian Flint [aut], Gurutzeta Guillera-Arroita [aut] Maintainer: Roozbeh Valavi <valavi.r at gmail.com> BugReports: https://github.com/rvalavi/blockCV/issues License: GPL (≥ 3) URL: https://github.com/rvalavi/blockCV NeedsCompilation: yes Citation: blockCV citation info In views: Spatial CRAN checks: blockCV results [issues need fixing before 2025-07-14]RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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