Package: spatialRF Title: Easy Spatial Modeling with Random Forest Version: 1.1.5 Authors@R: person(given = "Blas M.", family = "Benito", role = c("aut", "cre", "cph"), email = "blasbenito@gmail.com", comment = c(ORCID = "0000-0001-5105-7232")) URL: https://blasbenito.github.io/spatialRF/ BugReports: https://github.com/BlasBenito/spatialRF/issues/ Description: 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 ) or the RFsp approach (Hengl et al. ). 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 ). License: MIT + file LICENSE 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 Encoding: UTF-8 LazyData: true LazyDataCompression: xz Roxygen: list(markdown = TRUE, old_usage = FALSE) RoxygenNote: 7.3.3 Language: en-US Config/pak/sysreqs: texlive libicu-dev libxml2-dev Repository: https://blasbenito.r-universe.dev Date/Publication: 2026-06-25 07:15:09 UTC RemoteUrl: https://github.com/blasbenito/spatialrf RemoteRef: HEAD RemoteSha: b7ab6291d6e85872297ec98d722b7eae6b4cb138 NeedsCompilation: no Packaged: 2026-06-25 08:33:01 UTC; root Author: Blas M. Benito [aut, cre, cph] (ORCID: ) Maintainer: Blas M. Benito