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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 ORCID iD [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

Documentation:

Reference manual: spatialRF.html , spatialRF.pdf

Downloads:

Package source: spatialRF_1.1.5.tar.gz
Windows binaries: r-devel: spatialRF_1.1.5.zip, r-release: spatialRF_1.1.5.zip, r-oldrel: spatialRF_1.1.5.zip
macOS binaries: r-release (arm64): spatialRF_1.1.5.tgz, r-oldrel (arm64): spatialRF_1.1.5.tgz, r-release (x86_64): spatialRF_1.1.5.tgz, r-oldrel (x86_64): spatialRF_1.1.5.tgz
Old sources: spatialRF archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.