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.

Version: 0.1-2
Depends: R (≥ 2.14.0)
Imports: mboost, mgcv, grpreg, MASS
Published: 2017-07-23
Author: Madlene Nussbaum [cre, aut], Andreas Papritz [ths]
Maintainer: Madlene Nussbaum <madlene.nussbaum at env.ethz.ch>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: geoGAM results

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Reference manual: geoGAM.pdf
Package source: geoGAM_0.1-2.tar.gz
Windows binaries: r-devel: geoGAM_0.1-2.zip, r-release: geoGAM_0.1-2.zip, r-oldrel: geoGAM_0.1-2.zip
OS X El Capitan binaries: r-release: geoGAM_0.1-2.tgz
OS X Mavericks binaries: r-oldrel: geoGAM_0.1-2.tgz
Old sources: geoGAM archive

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