shrinkGPR
2.0.0Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
0packages depend
3.3Kdownloads / year
25.5%test coverage
13/13checks pass
Overview
About
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.
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Health
CRAN checks
13OK
Slowest check: 3.0 min · r-oldrel-windows-x86_64
Code health
Yes
Tests · ratio 0.10
25.5%
Coverage · measured lines
100%
Documentation · exports
7
Dependencies · direct
Check history
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
Documentation
READMENoVignettesNopkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
- Examples that run
- 17%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 9%
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Also on89 r2u
Dependencies
Declared dependencies
8 external dependencies (excludes base and recommended)
Depends (1)
R >= 4.1.0
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none
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none
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Package metadata
- First published
- 2025-01-30
- Total releases
- 4 / 1 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 53 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet