VBel-package {VBel}

Variational Bayes for Fast and Accurate Empirical Likelihood Inference

Description

Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.

Details

The DESCRIPTION file:

Package:	VBel
Type:	Package
Title:	Variational Bayes for Fast and Accurate Empirical Likelihood Inference
Version:	1.0.1
Date:	2024-05-28
Authors@R:	c( person("Wei-Chang", "Yu", , "[e-mail]", role = c("aut"), comment = c(ORCID = "0000-0002-0399-3779")), person("Jeremy", "Lim", , "[e-mail]", role = c("cre", "aut")) )
Description:	Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.
License:	GPL (>= 3)
Imports:	Rcpp (>= 1.0.12), stats
LinkingTo:	Rcpp, RcppEigen
Encoding:	UTF-8
Roxygen:	list(markdown = TRUE)
RoxygenNote:	7.3.1
URL:	https://github.com/jlimrasc/VBel
BugReports:	https://github.com/jlimrasc/VBel/issues
Suggests:	mvtnorm, testthat (>= 3.0.0)
Config/testthat/edition:	3
Author:	Wei-Chang Yu [aut] (<https://orcid.org/0000-0002-0399-3779>), Jeremy Lim [cre, aut]
Maintainer:	Jeremy Lim <[e-mail]>
Archs:	x64

Index of help topics:

VBel-package            Variational Bayes for Fast and Accurate
                        Empirical Likelihood Inference
compute_AEL             Compute the Adjusted Empirical Likelihood
compute_GVA             Compute the Full-Covariance Gaussian VB
                        Empirical Likelihood Posterior
diagnostic_plot         Check the convergence of a data set computed by
                        'compute_GVA'

Author(s)

Wei-Chang Yu [aut] (<https://orcid.org/0000-0002-0399-3779>), Jeremy Lim [cre, aut]

Maintainer: Jeremy Lim <[e-mail]>

References

https://www.tandfonline.com/doi/abs/10.1080/01621459.2023.2169701

See Also

compute_AEL() for choice of R and/or C++ computation of AEL

compute_GVA() for choice of R and/or C++ computation of GVA

diagnostic_plot() for verifying convergence of computed GVA data

Examples

#ansGVARcppPure <- compute_GVA(mu, C_0, h, delthh, delth_logpi, z, lam0, rho, 
#elip, a, iters, iters2, fullCpp = TRUE)
#diagnostic_plot(ansGVARcppPure)