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>).

Getting started

Package details

AuthorJakob A. Dambon [aut, cre] (<https://orcid.org/0000-0001-5855-2017>), Fabio Sigrist [ctb] (<https://orcid.org/0000-0002-3994-2244>), Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>)
MaintainerJakob A. Dambon <jakob.dambon@math.uzh.ch>
LicenseGPL-2
Version0.3.1
URL https://github.com/jakobdambon/varycoef
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("varycoef")

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varycoef documentation built on June 3, 2021, 5:10 p.m.