BayesGP: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models (original) (raw)
Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.
| Version: | 0.1.3 |
|---|---|
| Depends: | R (≥ 3.6.0) |
| Imports: | TMB (≥ 1.9.7), numDeriv, rstan, sfsmisc, Matrix (≥ 1.6.3), aghq (≥ 0.4.1), fda, tmbstan, LaplacesDemon, methods |
| LinkingTo: | TMB (≥ 1.9.7), RcppEigen |
| Suggests: | rmarkdown, knitr, survival, testthat (≥ 3.0.0) |
| Published: | 2024-11-12 |
| DOI: | 10.32614/CRAN.package.BayesGP |
| Author: | Ziang Zhang [aut, cre], Yongwei Lin [aut], Alex Stringer [aut], Patrick Brown [aut] |
| Maintainer: | Ziang Zhang |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | BayesGP results |
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