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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "bhetGP" in publications use:'
type: software
license: LGPL-2.0-only
title: 'bhetGP: Bayesian Heteroskedastic Gaussian Processes'
version: 1.0.2
doi: 10.32614/CRAN.package.bhetGP
abstract: 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) <https://doi.org/10.1080/10618600.2018.1458625>)
  For large data, Vecchia-approximation for faster computation is leveraged (Sauer,
  A., Cooper, A., and Gramacy, R., (2023), <https://doi.org/10.1080/10618600.2022.2129662>).
  Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the
  hood.
authors:
- family-names: Patil
  given-names: Parul V.
  email: parulvijay@vt.edu
repository: https://parulvpatil.r-universe.dev
commit: b16a326930dbf09414dd1dd6f7cc8da10e92c78d
date-released: '2026-02-09'
contact:
- family-names: Patil
  given-names: Parul V.
  email: parulvijay@vt.edu