shrinkGPR
Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
v2.0.0
·
Mar 30, 2026
·
GPL (>= 2)
Description
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>.
Downloads
CRAN
452
Last 30 days
8705th
1.5K
Last 90 days
3.2K
Last year
Trend: -5.2% (30d vs prior 30d)
r2u
CRAN
7
Last 30 days
16
Last 90 days
95
Last year
Trend: -22.2% (30d vs prior 30d)
CRAN Check Status
13
OK
Show all 13 flavors
| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | OK |
| r-devel-linux-x86_64-debian-gcc | OK |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check History
Archived
Mar 30, 2026