Easy Spatial Modeling with Random Forest R package spatialRF version 1.1.5 CRAN Task View: Spatial (https://CRAN.R-project.org/view=Spatial) Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>). Software Depends: R (>= 2.10) Imports: dplyr, ggplot2, magrittr, stats, tibble, utils, foreach, doParallel, ranger, rlang, tidyr, tidyselect, huxtable (>= 5.8.0), patchwork (>= 1.3.2), viridis Suggests: testthat, spelling Blas M. Benito <blasbenito@gmail.com> Comprehensive R Archive Network (CRAN) Blas M. Benito [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-5105-7232>) MIT + file LICENSE (https://CRAN.R-project.org/package=spatialRF/LICENSE) 2025-12-19 application/tgz https://CRAN.R-project.org/package=spatialRF doi:10.32614/CRAN.package.spatialRF en-US