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bhetGP

Bayesian Heteroskedastic Gaussian Processes

v1.0.2 · Feb 8, 2026 · LGPL

Description

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.

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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
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r-oldrel-windows-x86_64 OK
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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Apr 22, 2026
ERROR 13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Apr 18, 2026
ERROR r-devel-windows-x86_64

whether package can be installed

Installation failed.
See 'd:/Rcompile/CRANpkg/local/4.6/bhetGP.Rcheck/00install.out' for details.
OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies doParallel foreach GpGp GPvecchia Matrix Rcpp mvtnorm FNN hetGP laGP bhetGP

Version History

4 tracked
new 1.0.2 Mar 10, 2026
updated 1.0.2 ← 1.0.1 diff Feb 8, 2026
updated 1.0.1 ← 1.0 diff Jul 18, 2025
new 1.0 Jul 13, 2025