CRAN/E | blockCV

blockCV

Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation

Installation

About

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.

Citation blockCV citation info
github.com/rvalavi/blockCV
Bug report File report

Key Metrics

Version 3.1-3
R ≥ 3.5.0
Published 2023-06-04 298 days ago
Needs compilation? yes
License GPL (≥ 3)
CRAN checks blockCV results

Downloads

Yesterday 42 +27%
Last 7 days 230 -6%
Last 30 days 981 -12%
Last 90 days 3.170 +10%
Last 365 days 13.364 +21%

Maintainer

Maintainer

Roozbeh Valavi

valavi.r@gmail.com

Authors

Roozbeh Valavi

aut / cre

Jane Elith

aut

José Lahoz-Monfort

aut

Ian Flint

aut

Gurutzeta Guillera-Arroita

aut

Material

Reference manual
Package source

Vignettes

1. blockCV introduction: how to create block cross-validation folds
2. Block cross-validation for species distribution modelling

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

blockCV archive

Depends

R ≥ 3.5.0

Imports

sf ≥ 1.0
Rcpp ≥ 1.0.2

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

LinkingTo

Rcpp

Reverse Imports

forestecology
PointedSDMs

Reverse Suggests

BiodiversityR
ENMeval
mlr3spatiotempcv
sdmApp