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>). The package and its capabilities are described
in (Dambon et al. (2021c) <arXiv:2106.02364>).
| Version: |
0.3.3 |
| Depends: |
R (≥ 3.5.0), spam |
| Imports: |
glmnet, lhs, methods, mlr, mlrMBO, optimParallel (≥ 0.8-1), ParamHelpers, pbapply, smoof |
| Suggests: |
DiceKriging, gstat, parallel, spData, sp |
| Published: |
2022-05-31 |
| Author: |
Jakob A. Dambon 
[aut, cre],
Fabio Sigrist
[ctb],
Reinhard Furrer
[ctb] |
| Maintainer: |
Jakob A. Dambon <jakob.dambon at math.uzh.ch> |
| BugReports: |
https://github.com/jakobdambon/varycoef/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/jakobdambon/varycoef |
| NeedsCompilation: |
no |
| Citation: |
varycoef citation info |
| CRAN checks: |
varycoef results |
Documentation:
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