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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | gwrf_0.1.1.tar.gz |
49.0 KiB |
0.1.1 |
rolling linux/noble R-4.5 | gwrf_0.1.1.tar.gz |
48.9 KiB |
0.1.1 |
rolling source/ R- | gwrf_0.1.1.tar.gz |
11.9 KiB |
0.1.1 |
latest linux/jammy R-4.5 | gwrf_0.1.1.tar.gz |
49.0 KiB |
0.1.1 |
latest linux/noble R-4.5 | gwrf_0.1.1.tar.gz |
48.9 KiB |
0.1.1 |
latest source/ R- | gwrf_0.1.1.tar.gz |
11.9 KiB |
0.1.1 |
2026-04-23 source/ R- | gwrf_0.1.1.tar.gz |
0 B |