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>.

Version: 0.1-3
Depends: R (≥ 2.14.0)
Imports: mboost, mgcv, grpreg, MASS
Suggests: raster, sp
Published: 2023-11-14
DOI: 10.32614/CRAN.package.geoGAM
Author: Madlene Nussbaum [cre, aut], Andreas Papritz [ths]
Maintainer: Madlene Nussbaum <m.nussbaum at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: geoGAM results


Reference manual: geoGAM.pdf


Package source: geoGAM_0.1-3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): geoGAM_0.1-3.tgz, r-oldrel (arm64): geoGAM_0.1-3.tgz, r-release (x86_64): geoGAM_0.1-3.tgz, r-oldrel (x86_64): geoGAM_0.1-3.tgz
Old sources: geoGAM archive


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