BayesGP
0.1.3Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 1 earlier snapshots
- NOTE2026-03-1011 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.3Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2024-11-12
- Total releases
- 1 / 2 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.6.0
- Bundled data
- 91 KB / 3 files
- Download size
- 686 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
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citation("BayesGP")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
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