{ "_id": "6a101e05acfb0bcc41c8a80f", "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\" ),\nemail = \"m.nussbaum@uu.nl\" ),\nperson( \"Andreas\", \"Papritz\", role = c( \"ths\" ),\nemail = \"papritz at retired.ethz.ch\" ) )", "Description": "A model building procedure to build parsimonious\ngeoadditive model from a large number of covariates.\nContinuous, binary and ordered categorical responses are\nsupported. The model building is based on component wise\ngradient boosting with linear effects, smoothing splines and a\nsmooth spatial surface to model spatial autocorrelation. The\nresulting covariate set after gradient boosting is further\nreduced through backward elimination and aggregation of factor\nlevels. The package provides a model based bootstrap method to\nsimulate prediction intervals for point predictions. A test\ndata set of a soil mapping case study in Berne (Switzerland) is\nprovided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L.,\nand Papritz, A. 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