Package: bisque 1.0.2
bisque: Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models
Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) <arxiv:1904.07270> for complete details. 'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models. The resulting approximations are computationally efficient for many hierarchical Bayesian models. The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Authors:
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bisque.pdf |bisque.html✨
bisque/json (API)
NEWS
# Install 'bisque' in R: |
install.packages('bisque', repos = c('https://jmhewitt.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jmhewitt/bisque/issues
- furseals - Data from a capture-recapture study of fur seal pups
Last updated 5 years agofrom:2ed113b304. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 10 2024 |
R-4.5-win-x86_64 | NOTE | Nov 10 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 10 2024 |
R-4.4-win-x86_64 | NOTE | Nov 10 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 10 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 10 2024 |
R-4.3-win-x86_64 | NOTE | Nov 10 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 10 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 10 2024 |
Exports:createLocScaleGriddmixemixitxjac.expjac.invlogitjac.logjac.logitkComputelogjacsFitsKrigtxwBuildwMix
Dependencies:codetoolsdata.tableforeachiteratorsitertoolsmvQuadRcppRcppArmadilloRcppEigenstatmod