varycoef: 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) <doi:10.1016/j.spasta.2020.100470>). Covariance tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) 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) <arXiv:2101.01932>).

Version: 0.3.0
Depends: R (≥ 3.5.0), spam
Imports: DiceKriging, glmnet, lhs, mlr, mlrMBO, RandomFields, optimParallel (≥ 0.8-1), ParamHelpers, pbapply, smoof, sp
Suggests: gstat, knitr, microbenchmark, parallel, rmarkdown, R.rsp, spData
Published: 2021-01-13
Author: Jakob A. Dambon ORCID iD [aut, cre], Fabio Sigrist ORCID iD [ctb], Reinhard Furrer ORCID iD [ctb]
Maintainer: Jakob A. Dambon <jakob.dambon at>
License: GPL-2
NeedsCompilation: no
Citation: varycoef citation info
CRAN checks: varycoef results


Reference manual: varycoef.pdf
Vignettes: Manual
First Example
Package source: varycoef_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: varycoef_0.3.0.tgz, r-oldrel: varycoef_0.3.0.tgz
Old sources: varycoef archive


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