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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.
| Version: | 1.0 |
| Imports: | grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, GPvecchia, Matrix, Rcpp, mvtnorm, FNN, hetGP, laGP |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | interp |
| Published: | 2025-07-14 |
| DOI: | 10.32614/CRAN.package.bhetGP |
| Author: | Parul V. Patil [aut, cre] |
| Maintainer: | Parul V. Patil <parulvijay at vt.edu> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | bhetGP results [issues need fixing before 2025-07-29] |
| Reference manual: | bhetGP.pdf |
| Package source: | bhetGP_1.0.tar.gz |
| Windows binaries: | r-devel: bhetGP_1.0.zip, r-release: bhetGP_1.0.zip, r-oldrel: bhetGP_1.0.zip |
| macOS binaries: | r-release (arm64): bhetGP_1.0.tgz, r-oldrel (arm64): bhetGP_1.0.tgz, r-release (x86_64): bhetGP_1.0.tgz, r-oldrel (x86_64): bhetGP_1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.