BayesGOF: Bayesian Modeling via Frequentist Goodness-of-Fit

A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5>).

Version: 5.2
Depends: orthopolynom, VGAM, Bolstad2, nleqslv
Suggests: knitr, rmarkdown
Published: 2018-10-09
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: Bayesian Modeling via Frequentist Goodness-of-Fit
Package source: BayesGOF_5.2.tar.gz
Windows binaries: r-devel: BayesGOF_5.2.zip, r-release: BayesGOF_5.2.zip, r-oldrel: BayesGOF_5.2.zip
OS X binaries: r-release: BayesGOF_5.2.tgz, r-oldrel: BayesGOF_5.2.tgz
Old sources: BayesGOF archive

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