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>.

Version: 2.1.4
Depends: R (≥ 3.5.0)
Imports: raster (≥ 2.5-8), sf (≥ 0.8-0), progress
Suggests: knitr, ggplot2 (≥ 3.2.1), cowplot, automap (≥ 1.0-14), rgeos, rgdal, future, future.apply, shiny (≥ 1.0.3), shinydashboard, geosphere, methods, rmarkdown, testthat, covr
Published: 2021-06-17
Author: Roozbeh Valavi [aut, cre], Jane Elith [aut], José Lahoz-Monfort [aut], Gurutzeta Guillera-Arroita [aut]
Maintainer: Roozbeh Valavi <valavi.r at gmail.com>
License: GPL-3
URL: https://github.com/rvalavi/blockCV
NeedsCompilation: no
Citation: blockCV citation info
Materials: README NEWS
CRAN checks: blockCV results

Documentation:

Reference manual: blockCV.pdf
Vignettes: Block cross-validation for species distribution modelling

Downloads:

Package source: blockCV_2.1.4.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_2.1.4.tgz, r-release (x86_64): blockCV_2.1.4.tgz, r-oldrel: blockCV_2.1.4.tgz
Old sources: blockCV archive

Reverse dependencies:

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

Linking:

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