powerprior: Conjugate Power Priors for Bayesian Analysis of Normal Data
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).
| Version: |
1.0.0 |
| Imports: |
stats, MASS, LaplacesDemon, ggplot2, shiny, shinydashboard, shinyjs, DT, dplyr, tidyr, rlang |
| Suggests: |
testthat (≥ 3.0.0), rmarkdown |
| Published: |
2025-11-11 |
| DOI: |
10.32614/CRAN.package.powerprior (may not be active yet) |
| Author: |
Yusuke Yamaguchi [aut, cre],
Yifei Huang [aut] |
| Maintainer: |
Yusuke Yamaguchi <yamagubed at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
powerprior results |
Documentation:
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