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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=spatialRF
to link to this page.