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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.pdf
Vignettes: Bayesian Modeling via Frequentist Goodness-of-Fit

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:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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