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 <ziangzhang at uchicago.edu> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | BayesGP results |
| Reference manual: | BayesGP.pdf |
| Vignettes: |
BayesGP: Partial Likelihood (source, R code) BayesGP: COVID-19 Example (source, R code) BayesGP: Fitting sGP (source, R code) |
| Package source: | BayesGP_0.1.3.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
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