Conjugate Power Priors for Bayesian Analysis of Normal Data
Overview
Implements conjugate power priors for efficient Bayesian analysis of normal data. Power priors allow principled incorporation of historical information while controlling the degree of borrowing through a discounting parameter (Ibrahim and Chen (2000) doi:10.1214/ss/1009212519). This package provides closed-form conjugate representations for both univariate and multivariate normal data using Normal-Inverse-Chi-squared and Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. The conjugate framework builds upon standard Bayesian methods described in Gelman et al. (2013, ISBN:978-1439840955).
- Added a
NEWS.mdfile to track changes to the package.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 21 (1 internal)includes a package overview page
- Examples that run
- 92%11 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 92%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 10%
Downloads
Dependencies
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2025-11-11 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-11-11
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 33 KB
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