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: | 1.3 |
| Depends: | orthopolynom, VGAM |
| Published: | 2017-11-16 |
| Author: | Subhadeep Mukhopadhyay, Douglas Fletcher |
| Maintainer: | Doug Fletcher <tug25070 at temple.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | BayesGOF results |
| Reference manual: | BayesGOF.pdf |
| Package source: | BayesGOF_1.3.tar.gz |
| Windows binaries: | r-devel: BayesGOF_1.3.zip, r-release: BayesGOF_1.3.zip, r-oldrel: BayesGOF_1.3.zip |
| OS X El Capitan binaries: | r-release: BayesGOF_1.3.tgz |
| OS X Mavericks binaries: | r-oldrel: BayesGOF_1.3.tgz |
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