BayesGP

Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) doi:10.1177/09622802221134172; Zhang, Stringer, Brown, and Stafford (2024) doi:10.1080/10618600.2023.2289532; Zhang, Brown, and Stafford (2023) doi:10.48550/arXiv.2305.09914; and Stringer, Brown, and Stafford (2021) doi:10.1111/biom.13329.

Last 30 days

This package has been downloaded 507 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,558 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Feb 20, 2025 with 55 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.