https://hac-lab.r-universe.devRecent package updates in hac-labR-universehttps://github.com/hac-lab.pnghttps://hac-lab.r-universe.devWed, 26 Aug 2026 18:10:46 GMTerich_seamon@baylor.edu (Erich Seamon)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>.https://github.com/r-universe/hac-lab/actions/runs/40189497179Wed, 26 Aug 2026 18:10:46 GMTgwrf0.1.1.9000successhttps://hac-lab.r-universe.devhttps://github.com/hac-lab/gwrf