Modeling Spatially Varying Coefficients R package varycoef version 0.3.6 CRAN Task View: Spatial (https://CRAN.R-project.org/view=Spatial) 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) <doi:10.1080/13658816.2022.2097684>). The package and its capabilities are described in (Dambon et al. (2021c) <doi:10.48550/arXiv.2106.02364>). Software Depends: R (>= 3.5.0) Imports: glmnet, lhs, methods, mlr, mlrMBO, optimParallel (>= 0.8-1), ParamHelpers, pbapply, smoof, spam Suggests: DiceKriging, knitr, lattice, latticeExtra, parallel, rmarkdown, sp, spData, testthat (>= 3.0.0) Jakob A. Dambon <jakob.dambon@math.ethz.ch> Comprehensive R Archive Network (CRAN) Jakob A. Dambon [aut, cre] (ORCID: <https://orcid.org/0000-0001-5855-2017>), Fabio Sigrist [ctb] (ORCID: <https://orcid.org/0000-0002-3994-2244>), Reinhard Furrer [ctb] (ORCID: <https://orcid.org/0000-0002-6319-2332>) GPL-2 2025-05-04 application/tgz https://CRAN.R-project.org/package=varycoef doi:10.32614/CRAN.package.varycoef