  Rgbp is an R package that provides estimates and verifiable confidence
intervals for random effects in two-level conjugate hierarchical models for
overdispersed Gaussian, Poisson, and Binomial data. Rgbp models aggregate data
from k independent groups summarized by observed sufficient statistics for each
random effect, such as sample means, possibly with covariates. Rgbp uses
approximate Bayesian machinery with unique improper priors for the
hyper-parameters, which leads to good repeated sampling coverage properties for
random effects. A special feature of Rgbp is an option that generates synthetic
data sets to check whether the interval estimates for random effects actually
meet the nominal confidence levels. Additionally, Rgbp provides inference
statistics for the hyper-parameters, e.g., regression coefficients.
