VBel-package: Variational Bayes for Fast and Accurate Empirical Likelihood...

In VBel: Variational Bayes for Fast and Accurate Empirical Likelihood Inference

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>.

Author(s)

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

Maintainer: Jeremy Lim <[e-mail]>

References

References: Yu, W., & Bondell, H. D. (2023), “Variational Bayes for Fast and Accurate Empirical Likelihood Inference”, Journal of the American Statistical Association, 119(546), 1089–1101. Page 3 \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1080/01621459.2023.2169701")}

See Also

compute_AEL() for C++ computation of AEL

compute_GVA() for 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, SGD_iters, AEL_iters)
#diagnostic_plot(ansGVARcppPure)

VBel documentation built on Nov. 5, 2025, 5:14 p.m.