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bhetGP  

Bayesian Heteroskedastic Gaussian Processes
View on CRAN: Click here


Download and install bhetGP package within the R console
Install from CRAN:
install.packages("bhetGP")

Install from Github:
library("remotes")
install_github("cran/bhetGP")

Install by package version:
library("remotes")
install_version("bhetGP", "1.0.1")



Attach the package and use:
library("bhetGP")
Maintained by
Parul V. Patil
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-07-14
Latest Update: 2025-07-18
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.
How to cite:
Parul V. Patil (2025). bhetGP: Bayesian Heteroskedastic Gaussian Processes. R package version 1.0.1, https://cran.r-project.org/web/packages/bhetGP. Accessed 05 Jun. 2026.
Previous versions and publish date:
1.0.1 (2025-07-19 00:50), 1.0 (2025-07-14 19:20)
Other packages that cited bhetGP R package
View bhetGP citation profile
Other R packages that bhetGP depends, imports, suggests or enhances
Complete documentation for bhetGP
Functions, R codes and Examples using the bhetGP R package
Full bhetGP package functions and examples
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