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>).
Version: 0.3.6 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) Published: 2025-05-04 DOI: 10.32614/CRAN.package.varycoef Author: Jakob A. Dambon [aut, cre], Fabio Sigrist [ctb], Reinhard Furrer [ctb] Maintainer: Jakob A. Dambon <jakob.dambon at math.ethz.ch> BugReports: https://github.com/jakobdambon/varycoef/issues License: GPL-2 URL: https://github.com/jakobdambon/varycoef NeedsCompilation: no Citation: varycoef citation info In views: Spatial CRAN checks: varycoef results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=varycoef to link to this page.
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