CRAN/E | Rgbp

Rgbp

Hierarchical Modeling and Frequency Method Checking on Overdispersed Gaussian, Poisson, and Binomial Data

Installation

About

We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.

Citation Rgbp citation info
Bug report File report

Key Metrics

Version 1.1.4
R ≥ 2.2.0
Published 2019-12-17 1645 days ago
Needs compilation? no
License GPL-2
CRAN checks Rgbp results

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Maintainer

Maintainer

Joseph Kelly

Authors

Joseph Kelly
Hyungsuk Tak
Carl Morris

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

Rgbp archive

Depends

R ≥ 2.2.0

Imports

sn
mnormt