Fits geographically weighted random forest models using spatially localized training neighborhoods and 'ranger' as the random forest engine. Supports fixed-distance and adaptive neighborhoods defined by observation rows or unique spatial locations, including repeated observations at the same location. Provides local predictions and permutation-based variable importance for examining spatial variation in predictive relationships. The geographical random forest approach is described by Georganos et al. (2021) <doi:10.1080/10106049.2019.1595177>, and the 'ranger' engine by Wright and Ziegler (2017) <doi:10.18637/jss.v077.i01>.
| Version: | 0.1.1 |
| Imports: | ranger, tibble, dplyr, pbapply, stats |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2026-08-24 |
| DOI: | 10.32614/CRAN.package.gwrf |
| Author: | Erich Seamon [aut, cre, cph] |
| Maintainer: | Erich Seamon <erich_seamon at baylor.edu> |
| BugReports: | https://github.com/hac-lab/gwrf/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/hac-lab/gwrf |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | gwrf results |
| Reference manual: | gwrf.html , gwrf.pdf |
| Package source: | gwrf_0.1.1.tar.gz |
| Windows binaries: | r-devel: gwrf_0.1.1.zip, r-release: gwrf_0.1.1.zip, r-oldrel: gwrf_0.1.1.zip |
| macOS binaries: | r-release (arm64): gwrf_0.1.1.tgz, r-oldrel (arm64): gwrf_0.1.1.tgz, r-release (x86_64): gwrf_0.1.1.tgz, r-oldrel (x86_64): gwrf_0.1.1.tgz |
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