bhetGP
1.0.2Bayesian Heteroskedastic Gaussian Processes
Overview
Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) doi:10.1080/10618600.2018.1458625) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), doi:10.1080/10618600.2022.2129662). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood.
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
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 96%
- Return-value docs
- 100%
- References docs
- 57%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.2Latest
- 1.0.12025-07-19 · diff ↗
- 1.02025-07-14
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-07-14
- Total releases
- 3 / 1 yrs
- License
- LGPL
- Download size
- 62 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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