Modeling Spatially Varying Coefficients

Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian process-based spatially varying coefficient (SVC) models (Dambon et al. (2021a) ). Covariance tapering (Furrer et al. (2006) ) can be applied such that the method scales to large data. Further, it implements a joint variable selection of the fixed and random effects (Dambon et al. (2021b) ).


Reference manual

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0.3.0 by Jakob A. Dambon, 2 months ago

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Authors: Jakob A. Dambon [aut, cre] , Fabio Sigrist [ctb] , Reinhard Furrer [ctb]

Documentation:   PDF Manual  

GPL-2 license

Imports DiceKriging, glmnet, lhs, mlr, mlrMBO, RandomFields, optimParallel, ParamHelpers, pbapply, smoof, sp

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Suggests gstat, knitr, microbenchmark, parallel, rmarkdown, R.rsp, spData

See at CRAN