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bisque
View on CRAN: Click
here
Download and install bisque package within the R console
Install from CRAN:
install.packages("bisque")
Install from Github:
library("remotes")
install_github("cran/bisque")
Install by package version:
library("remotes")
install_version("bisque", "1.0.2")
Attach the package and use:
library("bisque")
Maintained by
Joshua Hewitt
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-24
Latest Update: 2020-02-06
Description:
Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) 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.
How to cite:
Joshua Hewitt (2019). bisque: Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models. R package version 1.0.2, https://cran.r-project.org/web/packages/bisque. Accessed 03 Dec. 2024.
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View bisque citation profile
Other R packages that bisque depends,
imports, suggests or enhances
Complete documentation for bisque
Functions, R codes and Examples using
the bisque R package
Some associated functions: createLocScaleGrid . dmix . emix . furseals . itx . jac.exp . jac.invlogit . jac.log . jac.logit . kCompute . logjac . mergePars . sFit . sKrig . tx . wBuild . wMix .
Some associated R codes: RcppExports.R . createLocScaleGrid.R . data.R . dmix.R . emix.R . globals.R . itx.R . jacobians.R . kCompute.R . logjac.R . mergePars.R . sFit.R . sKrig.R . tx.R . wBuild.R . wMix.R . Full bisque package functions and examples
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