Package: bhetGP Type: Package Title: Bayesian Heteroskedastic Gaussian Processes Version: 1.0.2 Authors@R: person(given = c("Parul", "V."), family = "Patil", role = c("aut", "cre"), email = "parulvijay@vt.edu") Maintainer: Parul V. Patil 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) ) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), ). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood. License: LGPL Encoding: UTF-8 NeedsCompilation: yes Imports: grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, GPvecchia, Matrix, Rcpp, mvtnorm, FNN, hetGP, laGP LinkingTo: Rcpp, RcppArmadillo Suggests: interp RoxygenNote: 7.3.2 Packaged: 2026-10-23 10:37:05 UTC; root Author: Parul V. Patil [aut, cre] Repository: https://cran.r-universe.dev Date/Publication: 2026-02-09 03:40:02 UTC RemoteUrl: https://github.com/cran/bhetGP RemoteRef: HEAD RemoteSha: b16a326930dbf09414dd1dd6f7cc8da10e92c78d