spatialRF
Easy Spatial Modeling with Random Forest
Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.5 |
rolling linux/jammy R-4.5 | spatialRF_1.1.5.tar.gz |
7.5 MiB |
1.1.5 |
rolling linux/noble R-4.5 | spatialRF_1.1.5.tar.gz |
7.5 MiB |
1.1.5 |
rolling source/ R- | spatialRF_1.1.5.tar.gz |
3.4 MiB |
1.1.5 |
latest linux/jammy R-4.5 | spatialRF_1.1.5.tar.gz |
7.5 MiB |
1.1.5 |
latest linux/noble R-4.5 | spatialRF_1.1.5.tar.gz |
7.5 MiB |
1.1.5 |
latest source/ R- | spatialRF_1.1.5.tar.gz |
3.4 MiB |
1.1.5 |
2026-04-26 source/ R- | spatialRF_1.1.5.tar.gz |
3.4 MiB |
1.1.5 |
2026-04-23 source/ R- | spatialRF_1.1.5.tar.gz |
3.4 MiB |
1.1.5 |
2026-04-09 windows/windows R-4.5 | spatialRF_1.1.5.zip |
7.6 MiB |
1.1.4 |
2025-04-20 source/ R- | spatialRF_1.1.4.tar.gz |
243.1 KiB |