BayesGOF: Bayesian Modeling via Goodness of Fit

Non-parametric method for learning prior distribution starting with parametric (subjective) prior. It performs four interconnected tasks: (i) characterizes the uncertainty of the elicited prior; (ii) exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) performs macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. (2017, Technical Report).

Version: 1.4
Depends: orthopolynom, VGAM
Suggests: knitr, rmarkdown
Published: 2018-01-05
Author: Subhadeep Mukhopadhyay, Douglas Fletcher
Maintainer: Doug Fletcher <tug25070 at temple.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: BayesGOF results

Downloads:

Reference manual: BayesGOF.pdf
Vignettes: Bayes via Goodness of Fit
Package source: BayesGOF_1.4.tar.gz
Windows binaries: r-devel: BayesGOF_1.4.zip, r-release: BayesGOF_1.4.zip, r-oldrel: BayesGOF_1.4.zip
OS X El Capitan binaries: r-release: BayesGOF_1.4.tgz
OS X Mavericks binaries: r-oldrel: BayesGOF_1.4.tgz
Old sources: BayesGOF archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BayesGOF to link to this page.