Package: ClinicalRobustPriors
Version: 1.1-2
Date: 2009-05-05
Title: Robust Bayesian Priors in Clinical Trials: An R Package for
Practitioners
Author: Jairo A. Fuquene P.
Maintainer: Jairo A. Fuquene P. <jairo.a.fuquene@uprrp.edu>
Description: 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 it 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 hyper-parameters and the posterior analysis
can be processed.
Depends: R (>= 2.7.2)
License: GPL-2 | GPL-3
Packaged: Tue May 5 13:30:11 2009; Usuario Profesor
Repository: CRAN
Date/Publication: 2009-05-05 18:43:19

Package: ClinicalRobustPriors
Version: 2.1-2
Date: 2009-07-23
Title: Robust Bayesian Priors in Clinical Trials: An R Package for
Practitioners
Author: Jairo A. Fuquene P.
Maintainer: Jairo A. Fuquene P. <jairo.a.fuquene@uprrp.edu>
Description: 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.
Depends: R (>= 2.7.2)
License: GPL-2 | GPL-3
Packaged: Thu Jul 23 17:49:51 2009; Usuario Profesor
Repository: CRAN
Date/Publication: 2009-07-24 06:04:20