BayesPPD: Bayesian Power Prior Design

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 <doi:10.32614/RJ-2023-016>. 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>.

Version: 1.1.2
Depends: R (≥ 3.5.0)
Imports: Rcpp
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppNumerical
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), ggplot2, kableExtra
Published: 2023-11-25
Author: Yueqi Shen [aut, cre], Matthew A. Psioda [aut], Joseph G. Ibrahim [aut]
Maintainer: Yueqi Shen <ys137 at live.unc.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: NEWS
CRAN checks: BayesPPD results

Documentation:

Reference manual: BayesPPD.pdf
Vignettes: bayesppd-vignette

Downloads:

Package source: BayesPPD_1.1.2.tar.gz
Windows binaries: r-devel: BayesPPD_1.1.2.zip, r-release: BayesPPD_1.1.2.zip, r-oldrel: BayesPPD_1.1.2.zip
macOS binaries: r-release (arm64): BayesPPD_1.1.2.tgz, r-oldrel (arm64): BayesPPD_1.1.2.tgz, r-release (x86_64): BayesPPD_1.1.2.tgz
Old sources: BayesPPD archive

Reverse dependencies:

Reverse suggests: psborrow2

Linking:

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