shrinkGPR
2.0.0Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
Overview
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.
Install
Health
- OK2026-08-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 17%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 9%
Downloads
Dependencies
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Code & Tests
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4 releases. Pick two to compare their code metrics. R releases are shown for context.
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
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