Package: geoGAM
Type: Package
Title: Select Sparse Geoadditive Models for Spatial Prediction
Version: 0.1-2
Date: 2017-07-23
Authors@R: c( person( "Madlene", "Nussbaum", role = c( "cre", "aut" ),
          email =  "madlene.nussbaum@env.ethz.ch" ),
	      person( "Andreas", "Papritz", role = c( "ths" ),
          email =  "andreas.papritz@env.ethz.ch" ) )
Depends: R(>= 2.14.0)
Imports: mboost, mgcv, grpreg, MASS
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. 
License: GPL (>= 2)
Author: Madlene Nussbaum [cre, aut], Andreas Papritz [ths]
Maintainer: Madlene Nussbaum <madlene.nussbaum@env.ethz.ch>
LazyData: TRUE
NeedsCompilation: no
Repository: CRAN
Packaged: 2017-07-23 13:50:46 UTC; nam1
Date/Publication: 2017-07-23 16:25:17 UTC
Built: R 4.0.0; ; 2020-04-10 08:17:18 UTC; windows
