Package: geoGAM
Type: Package
Title: Select Sparse Geoadditive Models for Spatial Prediction
Version: 0.1-4
Date: 2025-10-12
Authors@R: c( person( "Madlene", "Nussbaum", role = c( "cre", "aut" ),
          email =  "m.nussbaum@uu.nl" ),
	      person( "Andreas", "Papritz", role = c( "ths" ),
          email =  "papritz at retired.ethz.ch" ) )
Depends: R(>= 2.14.0)
Imports: mboost, mgcv, grpreg, MASS
Suggests: raster, sp
Description: 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>. 
License: GPL (>= 2)
Author: Madlene Nussbaum [cre, aut],
  Andreas Papritz [ths]
Maintainer: Madlene Nussbaum <m.nussbaum@uu.nl>
LazyData: TRUE
NeedsCompilation: no
Repository: CRAN
Packaged: 2025-10-12 18:47:36 UTC; madlene
Date/Publication: 2025-10-16 07:30:02 UTC
Built: R 4.6.0; ; 2025-10-17 23:51:49 UTC; windows
