ClinicalRobustPriors: Robust Bayesian Priors in Clinical Trials: An R Package for Practitioners

In a recent paper, Fuquene, Cook, & Pericchi (2008) (http://www.bepress.com/mdandersonbiostat/paper44 ) make a comprehensive proposal putting forward robust, heavy-tailed priors over conjugate, light-tailed priors in Bayesian analysis. The behavior of Robust Bayesian methods is qualitative different than Conjugate and short tailed Bayesian methods and arguably much more reasonable and acceptable to the practitioner and regulatory agencies. This package is useful to compute the distributions (prior, likelihood and posterior) and moments of the robust models: Cauchy/Binomial, Cauchy/Normal and Berger/Normal. Both, Binomial and Normal Likelihoods can be handled by the software. Furthermore, the assessment of the hyperparameters and the posterior analysis can be processed.

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