Spatial Cross-Validation for Machine Learning R package spatialcvR version 0.1.0 Spatial cross-validation and model evaluation for geospatial machine learning applications. Addresses spatial dependence in observations by implementing spatial block, buffered, and clustering cross-validation methods. Includes spatial leakage detection, model performance metrics, and spatial residual diagnostics for assessing model generalization across geographic space. Methods based on Brenning (2012) <doi:10.1016/j.cageo.2012.02.001>, Pohjankukka et al. (2017) <doi:10.1016/j.isprsjprs.2017.07.001>, and Roberts et al. (2017) <doi:10.1111/ecog.02881>. Software Depends: R (>= 3.5.0) Imports: sf (>= 1.0-0) Suggests: dplyr (>= 1.0.0), ggplot2 (>= 3.4.0), tidymodels (>= 1.0.0), rsample (>= 1.0.0), testthat (>= 3.0.0), knitr (>= 1.40), rmarkdown (>= 2.14), terra (>= 1.5.0) Mamadou SOW <sowsalim01@gmail.com> Comprehensive R Archive Network (CRAN) Mamadou SOW [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=spatialcvR/LICENSE) 2026-10-07 application/tgz https://CRAN.R-project.org/package=spatialcvR doi:10.32614/CRAN.package.spatialcvR