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
Packaged: 2026-07-13 07:53:33 UTC; root
Repository: https://nussbaummadlene.r-universe.dev
Date/Publication: 2025-10-16 07:30:02 UTC
RemoteUrl: https://github.com/cran/geoGAM
RemoteRef: HEAD
RemoteSha: 50deb0131218380d6626a0150d0d36e22377f18f