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) ).


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install.packages("varycoef")

0.3.1 by Jakob A. Dambon, 13 days ago


https://github.com/jakobdambon/varycoef


Report a bug at https://github.com/jakobdambon/varycoef/issues


Browse source code at https://github.com/cran/varycoef


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


Documentation:   PDF Manual  


GPL-2 license


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

Depends on spam

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


See at CRAN