Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian processbased 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) <arXiv:2106.02364>).
Package details 


Author  Jakob A. Dambon [aut, cre] (<https://orcid.org/0000000158552017>), Fabio Sigrist [ctb] (<https://orcid.org/0000000239942244>), Reinhard Furrer [ctb] (<https://orcid.org/0000000263192332>) 
Maintainer  Jakob A. Dambon <jakob.dambon@math.uzh.ch> 
License  GPL2 
Version  0.3.4 
URL  https://github.com/jakobdambon/varycoef 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.