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) <doi:10.48550/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.

Version:	1.0.2
Depends:	R (≥ 3.0.2)
Imports:	mvQuad, Rcpp, foreach, itertools
LinkingTo:	Rcpp (≥ 0.12.4), RcppArmadillo, RcppEigen (≥ 0.3.3.3.1)
Suggests:	testthat, fields
Published:	2020-02-06
DOI:	10.32614/CRAN.package.bisque
Author:	Joshua Hewitt
Maintainer:	Joshua Hewitt <joshua.hewitt at duke.edu>
License:	GPL-3
NeedsCompilation:	yes
SystemRequirements:	A system with a recent-enough C++11 compiler (such as g++-4.8 or later).
Materials:	NEWS
CRAN checks:	bisque results

Documentation:

Reference manual:

bisque.pdf

Downloads:

Package source:	bisque_1.0.2.tar.gz
Windows binaries:	r-devel: bisque_1.0.2.zip, r-release: bisque_1.0.2.zip, r-oldrel: bisque_1.0.2.zip
macOS binaries:	r-release (arm64): bisque_1.0.2.tgz, r-oldrel (arm64): bisque_1.0.2.tgz, r-release (x86_64): bisque_1.0.2.tgz, r-oldrel (x86_64): bisque_1.0.2.tgz
Old sources:	bisque archive

Linking:

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