Bayesian Heteroskedastic Gaussian Processes R package bhetGP version 1.0.2 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. Software Imports: grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, GPvecchia, Matrix, Rcpp, mvtnorm, FNN, hetGP, laGP LinkingTo: Rcpp, RcppArmadillo Suggests: interp Parul V. Patil <parulvijay@vt.edu> Comprehensive R Archive Network (CRAN) Parul V. Patil [aut, cre] LGPL 2026-02-09 application/tgz https://CRAN.R-project.org/package=bhetGP doi:10.32614/CRAN.package.bhetGP