Package: BayesPPD
Title: Bayesian Power Prior Design
Version: 1.1.0
Date: 2022-11-11
Authors@R: 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"))
Description: 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>. 
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.2.1
LazyData: true
Imports: Rcpp
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppNumerical
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), ggplot2, kableExtra
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2022-11-12 05:19:09 UTC; angie
Author: Yueqi Shen [aut, cre],
  Matthew A. Psioda [aut],
  Joseph G. Ibrahim [aut]
Maintainer: Yueqi Shen <ys137@live.unc.edu>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2022-11-12 08:20:02 UTC
Built: R 4.1.1; aarch64-apple-darwin20; 2022-11-13 13:16:59 UTC; unix
Archs: BayesPPD.so.dSYM
