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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>.

Versions across snapshots

VersionRepositoryFileSize
2.0.0 rolling linux/jammy R-4.5 shrinkGPR_2.0.0.tar.gz 312.7 KiB
2.0.0 rolling linux/noble R-4.5 shrinkGPR_2.0.0.tar.gz 313.0 KiB
2.0.0 rolling source/ R- shrinkGPR_2.0.0.tar.gz 89.4 KiB
2.0.0 latest linux/jammy R-4.5 shrinkGPR_2.0.0.tar.gz 312.7 KiB
2.0.0 latest linux/noble R-4.5 shrinkGPR_2.0.0.tar.gz 313.0 KiB
2.0.0 latest source/ R- shrinkGPR_2.0.0.tar.gz 89.4 KiB
2.0.0 2026-04-26 source/ R- shrinkGPR_2.0.0.tar.gz 89.4 KiB
2.0.0 2026-04-23 source/ R- shrinkGPR_2.0.0.tar.gz 89.4 KiB
2.0.0 2026-04-09 windows/windows R-4.5 shrinkGPR_2.0.0.zip 315.9 KiB
1.0.0 2025-04-20 source/ R- shrinkGPR_1.0.0.tar.gz 27.5 KiB

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