Package: Rgbp
Version: 1.1.3
Date: 2018-05-16
Title: Hierarchical Modeling and Frequency Method Checking on
        Overdispersed Gaussian, Poisson, and Binomial Data
Author: Joseph Kelly, Hyungsuk Tak, and Carl Morris
Maintainer: Joseph Kelly <josephkelly@post.harvard.edu>
Depends: R (>= 2.2.0)
Imports: sn, mnormt
Description: 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.
License: GPL-2
BugReports: https://github.com/jyklly/Rgbp/issues
Repository: https://jyklly.r-universe.dev
Date/Publication: 2018-05-17 15:24:00 UTC
RemoteUrl: https://github.com/jyklly/rgbp
RemoteRef: HEAD
RemoteSha: 79b12bfa3df63e91ae961b083a5ea3fdf4c3e975
RemoteSubdir: Rgbp
NeedsCompilation: no
Packaged: 2026-10-12 11:49:38 UTC; root