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 <parulvijay@vt.edu>
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.
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: 2026-02-09 01:46:08 UTC; parulvijay
Author: Parul V. Patil [aut, cre]
Repository: CRAN
Date/Publication: 2026-02-09 03:40:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-27 23:35:23 UTC; unix
Archs: bhetGP.so.dSYM
