Package: bhetGP 1.0.2

Parul V. Patil

bhetGP: Bayesian Heteroskedastic Gaussian Processes

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.

Authors:Parul V. Patil [aut, cre]

bhetGP_1.0.2.tar.gz
bhetGP_1.0.2.tar.gz(r-4.7-arm64)bhetGP_1.0.2.tar.gz(r-4.7-x86_64)bhetGP_1.0.2.tar.gz(r-4.6-arm64)bhetGP_1.0.2.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
bhetGP/json (API)

# Install 'bhetGP' in R:
install.packages('bhetGP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

2.00 score 571 downloads 3 exports 30 dependencies

Last updated from:b16a326930. Checks:5 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK162
linux-devel-x86_64OK171
source / vignettesOK208
linux-release-arm64OK168
linux-release-x86_64OK164
wasm-releaseFAIL138

Exports:bhetGPbhomGPtrim

Dependencies:BHclustercodetoolsDiceDesigndoParalleldotCall64fieldsFNNforeachGpGpGPvecchiahetGPiteratorslaGPlatticemapsmaptreeMASSMatrixmcomvtnormquadprogRColorBrewerRcppRcppArmadillorpartspamsparseinvtgpviridisLite