c(
person(given = "Yueqi",
family = "Shen",
role = c("aut","cre"),
email = "ys137@live.unc.edu"),
person(given = "Matthew A.",
family = "Psioda",
role = c("aut"),
email = "matt_psioda@unc.edu"),
person(given = "Joseph G.",
family = "Ibrahim",
role = c("aut"),
email = "ibrahim@bios.unc.edu"))
Yueqi Shen [aut, cre],
Matthew A. Psioda [aut],
Joseph G. Ibrahim [aut]

Bayesian power/type I error calculation and model fitting using
the power prior and the normalized power prior for generalized linear models.
Detailed examples of applying the package are available at <arXiv:2112.14616>.
The Bayesian clinical trial design methodology is described in Chen et al. (2011)
<doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019)
<doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006)
<doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.

rmarkdown, knitr, testthat (>= 3.0.0), ggplot2, kableExtra

Bayesian Methods for Panel Data Modeling and Inference
Chunhua Wu <chunhuawu@wustl.edu>, Siddhartha Chib
<chib@wustl.edu>
This package contains functions for panel data modeling
and inference using Bayesian methods. It is a realization of
models described in Chib(2008): Panel Data Modeling and
Inference: A Bayesian Primer. Most functions utilized the GNU
Scientific Library (GSL).

Ander Wilson, Howard D. Bondell, and Brian J. Reich
Fits the Bayesian penalized credible region variable and confounder selection methods.

Variable Selection in a Hierarchical Bayesian Model for a Hazard
Function
person("Andrew", "Chapple", email = "AndrewChapple21@gmail.com", role = c("aut", "cre"))
Fits a piecewise exponential hazard to survival data using a
Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
formulation for the spatial dependency in the hazard rates for each piece.
This function uses Metropolis- Hastings-Green MCMC to allow the number of split
points to vary and also uses Stochastic Search Variable Selection to determine
what covariates drive the risk of the event. This function outputs trace plots
depicting the number of split points in the hazard and the number of variables
included in the hazard. The function saves all posterior quantities to the
desired path.