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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "gwrf" in publications use:'
type: software
license: MIT
title: 'gwrf: Geographically Weighted Random Forests'
version: 0.1.1.9000
doi: 10.32614/CRAN.package.gwrf
abstract: 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) <https://doi.org/10.1080/10106049.2019.1595177>, and the 'ranger'
  engine by Wright and Ziegler (2017) <https://doi.org/10.18637/jss.v077.i01>.
authors:
- family-names: Seamon
  given-names: Erich
  email: erich_seamon@baylor.edu
repository: https://hac-lab.r-universe.dev
repository-code: https://github.com/hac-lab/gwrf
commit: 611d16f5cfbc16350e0585dccabe1f3d74991087
url: https://github.com/hac-lab/gwrf
date-released: '2026-08-26'
contact:
- family-names: Seamon
  given-names: Erich
  email: erich_seamon@baylor.edu