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>.
Package details |
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| Author | Erich Seamon [aut, cre, cph] |
| Maintainer | Erich Seamon <erich_seamon@baylor.edu> |
| License | MIT + file LICENSE |
| Version | 0.1.1 |
| URL | https://github.com/hac-lab/gwrf |
| Package repository | View on CRAN |
| Installation |
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