Package: shrinkGPR 2.0.1

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>.

Authors:Peter Knaus [aut, cre]

shrinkGPR_2.0.1.tar.gz
shrinkGPR_2.0.1.zip(r-4.7)shrinkGPR_2.0.1.zip(r-4.6)shrinkGPR_2.0.1.zip(r-4.5)
shrinkGPR_2.0.1.tgz(r-4.6-any)shrinkGPR_2.0.1.tgz(r-4.5-any)
shrinkGPR_2.0.1.tar.gz(r-4.7-any)shrinkGPR_2.0.1.tar.gz(r-4.6-any)
shrinkGPR_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
shrinkGPR/json (API)
NEWS

# Install 'shrinkGPR' in R:

install.packages('shrinkGPR', repos = c('https://neferkareii.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/neferkareii/shrinkgpr/issues

On CRAN:

3.60 score 1 stars 3 scripts 463 downloads 18 exports 32 dependencies

Last updated from:7bf15e5629. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	161
source / vignettes	OK	213
linux-release-x86_64	OK	162
macos-release-arm64	OK	150
macos-oldrel-arm64	OK	181
windows-devel	OK	143
windows-release	OK	109
windows-oldrel	OK	93
wasm-release	OK	125

Exports:calc_pred_moments eval_pred_dens gen_marginal_samples gen_posterior_samples kernel_matern_12 kernel_matern_32 kernel_matern_52 kernel_se load_shrinkGPR LPDS save_shrinkGPR shrinkGPR shrinkMVGPR shrinkMVTPR shrinkTPR simGPR simMVGPR sylvester

Dependencies:bit bit64 callr cli coro crayon desc farver glue gsl hms jsonlite labeling lifecycle magrittr mniw pkgconfig prettyunits processx progress ps R6 RColorBrewer Rcpp RcppEigen rlang safetensors scales torch vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate Predictive Moments	calc_pred_moments
Evaluate Predictive Densities	eval_pred_dens
Generate Marginal Samples of Predictive Distribution	gen_marginal_samples
Generate Posterior Samples	gen_posterior_samples
Kernel Functions for Gaussian and t Processes	kernel_functions kernel_matern_12 kernel_matern_32 kernel_matern_52 kernel_se
Load a saved shrinkGPR model object from disk	load_shrinkGPR
Log Predictive Density Score	LPDS
Graphical summary of posterior of theta	plot.shrinkGPR
Plot method for 1D marginal predictions	plot.shrinkGPR_marg_samples_1D
Plot method for 2D marginal predictions	plot.shrinkGPR_marg_samples_2D
Graphical summary of posterior of theta	plot.shrinkMVGPR
Graphical summary of posterior of theta	plot.shrinkTPR
Generate Predictions	predict.shrinkGPR
Generate Predictions	predict.shrinkMVGPR
Generate Predictions	predict.shrinkTPR
Save a fitted shrinkGPR model object to disk	save_shrinkGPR
Gaussian Process Regression with Shrinkage and Normalizing Flows	shrinkGPR
Multivariate Gaussian Process Regression with Shrinkage and Normalizing Flows	shrinkMVGPR
Multivariate Student-t Process Regression with Shrinkage and Normalizing Flows	shrinkMVTPR
Student-t Process Regression with Shrinkage and Normalizing Flows	shrinkTPR
Simulate Data for Gaussian Process Regression	simGPR
Simulate Data for Multivariate Gaussian Process Regression	simMVGPR
Sylvester Normalizing Flow	sylvester