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.

Version: 1.0.1
Depends: R (≥ 3.0.2)
Imports: mvQuad, Rcpp, doRNG, foreach, itertools
LinkingTo: Rcpp (≥ 0.12.4), RcppArmadillo, RcppEigen (≥ 0.3.3.3.1)
Suggests: testthat, fields
Published: 2019-04-26
Author: Joshua Hewitt
Maintainer: Joshua Hewitt <joshua.hewitt at colostate.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

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Reference manual: bisque.pdf
Package source: bisque_1.0.1.tar.gz
Windows binaries: r-devel: bisque_1.0.1.zip, r-devel-gcc8: bisque_1.0.1.zip, r-release: bisque_1.0.1.zip, r-oldrel: bisque_1.0.1.zip
OS X binaries: r-release: bisque_1.0.1.tgz, r-oldrel: bisque_1.0.1.tgz
Old sources: bisque archive

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