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Rgbp is an R package that provides estimates and verifiable confidenceintervals for random effects in two-level conjugate hierarchical models foroverdispersed Gaussian, Poisson, and Binomial data. Rgbp models aggregate datafrom k independent groups summarized by observed sufficient statistics for eachrandom effect, such as sample means, possibly with covariates. Rgbp usesapproximate Bayesian machinery with unique improper priors for thehyper-parameters, which leads to good repeated sampling coverage properties forrandom effects. A special feature of Rgbp is an option that generates syntheticdata sets to check whether the interval estimates for random effects actuallymeet the nominal confidence levels. Additionally, Rgbp provides inferencestatistics for the hyper-parameters, e.g., regression coefficients.

Rgbp: An R Package for Gaussian, Poisson, and Binomial Random Effects Models with Frequency Coverage Evaluations