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:Joshua Hewitt

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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'))

Peer review:

Bug tracker:https://github.com/jmhewitt/bisque/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • furseals - Data from a capture-recapture study of fur seal pups

On CRAN:

3.18 score 1 stars 10 scripts 209 downloads 3 mentions 15 exports 10 dependencies

Last updated 5 years agofrom:2ed113b304. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-win-x86_64NOTENov 10 2024
R-4.5-linux-x86_64NOTENov 10 2024
R-4.4-win-x86_64NOTENov 10 2024
R-4.4-mac-x86_64NOTENov 10 2024
R-4.4-mac-aarch64NOTENov 10 2024
R-4.3-win-x86_64NOTENov 10 2024
R-4.3-mac-x86_64NOTENov 10 2024
R-4.3-mac-aarch64NOTENov 10 2024

Exports:createLocScaleGriddmixemixitxjac.expjac.invlogitjac.logjac.logitkComputelogjacsFitsKrigtxwBuildwMix

Dependencies:codetoolsdata.tableforeachiteratorsitertoolsmvQuadRcppRcppArmadilloRcppEigenstatmod