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shrinkGPR (version 1.0.0)

Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Description

Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior 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) .

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Version

Install

install.packages('shrinkGPR')

Monthly Downloads

139

Version

1.0.0

License

GPL (>= 2)

Maintainer

Peter Knaus

Last Published

January 30th, 2025

Functions in shrinkGPR (1.0.0)

LPDS

Log Predictive Density Score
predict.shrinkGPR

Generate Predictions
simGPR

Simulate Data for Gaussian Process Regression
kernel_functions

Kernel Functions for Gaussian Processes
calc_pred_moments

Calculate Predictive Moments
shrinkGPR

Gaussian Process Regression with Shrinkage and Normalizing Flows
sylvester

Sylvester Normalizing Flow
gen_posterior_samples

Generate Posterior Samples
eval_pred_dens

Evaluate Predictive Densities