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 <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>).
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
RoxygenNote: 7.3.3
Language: en-US
NeedsCompilation: no
Packaged: 2025-12-19 20:53:15 UTC; blas
Author: Blas M. Benito [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0001-5105-7232>)
Maintainer: Blas M. Benito <blasbenito@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-19 23:40:02 UTC
Built: R 4.6.0; ; 2026-04-30 04:06:15 UTC; unix
