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>.

Version: 3.0-0
Depends: R (≥ 3.5.0)
Imports: sf (≥ 1.0), Rcpp (≥ 1.0.2)
LinkingTo: Rcpp
Suggests: terra (≥ 1.6-41), raster (≥ 2.5-8), ggplot2 (≥ 3.3.6), cowplot, automap (≥ 1.0-16), shiny (≥ 1.7), tmap (≥ 2.0), methods, knitr, rmarkdown, testthat (≥ 3.0.0), covr
Published: 2023-02-06
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
CRAN checks: blockCV results

Documentation:

Reference manual: blockCV.pdf
Vignettes: 1. blockCV introduction: how to create block cross-validation folds

Downloads:

Package source: blockCV_3.0-0.tar.gz
Windows binaries: r-devel: blockCV_2.1.4.zip, r-release: blockCV_2.1.4.zip, r-oldrel: blockCV_2.1.4.zip
macOS binaries: r-release (arm64): blockCV_3.0-0.tgz, r-oldrel (arm64): blockCV_3.0-0.tgz, r-release (x86_64): blockCV_2.1.4.tgz, r-oldrel (x86_64): blockCV_2.1.4.tgz
Old sources: blockCV archive

Reverse dependencies:

Reverse imports: forestecology, PointedSDMs
Reverse suggests: BiodiversityR, ENMeval, mlr3spatiotempcv, sdmApp

Linking:

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