spatialRF: Easy Spatial Modeling with Random Forest

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>).

Version:	1.1.5
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
Published:	2025-12-19
DOI:	10.32614/CRAN.package.spatialRF
Author:	Blas M. Benito [aut, cre, cph]
Maintainer:	Blas M. Benito <blasbenito at gmail.com>
BugReports:	https://github.com/BlasBenito/spatialRF/issues/
License:	MIT + file LICENSE
URL:	https://blasbenito.github.io/spatialRF/
NeedsCompilation:	no
Language:	en-US
Citation:	spatialRF citation info
Materials:	NEWS
In views:	Spatial
CRAN checks:	spatialRF results

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