Package: shrinkGPR 2.1.1

shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Efficient variational inference methods for fully Bayesian univariate and multivariate Gaussian and t-process regression models. Hierarchical shrinkage priors, including the triple gamma prior, are used for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.

Authors:Peter Knaus [aut, cre]

shrinkGPR_2.1.1.tar.gz
shrinkGPR_2.1.1.zip(r-4.7-any)shrinkGPR_2.1.1.zip(r-4.6-any)shrinkGPR_2.1.1.zip(r-4.5-any)
shrinkGPR_2.1.1.tgz(r-4.6-any)shrinkGPR_2.1.1.tgz(r-4.5-any)
shrinkGPR_2.1.1.tar.gz(r-4.7-any)shrinkGPR_2.1.1.tar.gz(r-4.6-any)
shrinkGPR_2.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
shrinkGPR/json (API)

# Install 'shrinkGPR' in R:
install.packages('shrinkGPR', repos = c('https://neferkareii.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/neferkareii/shrinkgpr/issues

On CRAN:

Conda:

3.40 score 1 stars 4 scripts 297 downloads 20 exports 33 dependencies

Last updated from:bb488f5b85. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK184
source / vignettesOK223
linux-releaseOK212
macos-releaseOK141
macos-oldrelOK85
windows-develOK103
windows-releaseOK96
windows-oldrelOK97
wasm-releaseOK117

Exports:calc_pred_momentseval_pred_densgen_marginal_samplesgen_posterior_samplesiafkernel_matern_12kernel_matern_32kernel_matern_52kernel_seload_shrinkGPRLPDSnsf_clsave_shrinkGPRshrinkGPRshrinkMVGPRshrinkMVTPRshrinkTPRsimGPRsimMVGPRsylvester

Dependencies:bitbit64callrclicorocrayondescfarvergluegslhmsjsonlitelabelinglifecyclemagrittrmniwotelpkgconfigprettyunitsprocessxprogresspsR6RColorBrewerRcppRcppEigenrlangsafetensorsscalestorchvctrsviridisLitewithr