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) , and the 'ranger' engine by Wright and Ziegler (2017) .

gwrf

gwrf is an R package for fitting geographically weighted random forest models with local spatial neighborhoods, kernel weighting, local predictions, residuals, and local variable importance.

Basic development use

devtools::load_all("/path/to/gwrf")

## Core function

fit_gwrf()