geoGAM
Select Sparse Geoadditive Models for Spatial Prediction
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1-4 |
rolling source/ R- | geoGAM_0.1-4.tar.gz |
3.1 MiB |
0.1-4 |
rolling linux/jammy R-4.5 | geoGAM_0.1-4.tar.gz |
4.5 MiB |
0.1-4 |
latest source/ R- | geoGAM_0.1-4.tar.gz |
3.1 MiB |
0.1-4 |
latest linux/jammy R-4.5 | geoGAM_0.1-4.tar.gz |
4.5 MiB |
0.1-4 |
2026-04-23 source/ R- | geoGAM_0.1-4.tar.gz |
3.1 MiB |
0.1-4 |
2026-04-09 windows/windows R-4.5 | geoGAM_0.1-4.zip |
4.5 MiB |
0.1-3 |
2025-04-20 source/ R- | geoGAM_0.1-3.tar.gz |
3.1 MiB |