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
DOI:	10.32614/CRAN.package.BayesGOF
Author:	Subhadeep Mukhopadhyay, Douglas Fletcher
Maintainer:	Doug Fletcher <tug25070 at temple.edu>
License:	GPL-2
NeedsCompilation:	no
In views:	Bayesian
CRAN checks:	BayesGOF results

Documentation:

Reference manual:	BayesGOF.html , BayesGOF.pdf
Vignettes:	Bayesian Modeling via Frequentist Goodness-of-Fit (source, R code)

Downloads:

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
macOS binaries:	r-release (arm64): BayesGOF_5.2.tgz, r-oldrel (arm64): BayesGOF_5.2.tgz, r-release (x86_64): BayesGOF_5.2.tgz, r-oldrel (x86_64): BayesGOF_5.2.tgz
Old sources:	BayesGOF archive

Reverse dependencies:

Reverse depends:

LPRelevance

Linking:

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