gwrf: Geographically Weighted Random Forests

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 (may not be active yet)
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

Documentation:

Reference manual:

gwrf.html , gwrf.pdf

Downloads:

Package source:	gwrf_0.1.1.tar.gz
Windows binaries:	r-devel: gwrf_0.1.1.zip, r-release: not available, r-oldrel: not available
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

Linking:

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