Package: BayesPPD 1.1.3

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>. Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. 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>.

Authors:Yueqi Shen [aut, cre], Matthew A. Psioda [aut], Joseph G. Ibrahim [aut]

BayesPPD_1.1.3.tar.gz
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BayesPPD_1.1.3.tgz(r-4.6-x86_64)BayesPPD_1.1.3.tgz(r-4.6-arm64)BayesPPD_1.1.3.tgz(r-4.5-x86_64)BayesPPD_1.1.3.tgz(r-4.5-arm64)
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BayesPPD_1.1.3.tgz(r-4.5-emscripten)
BayesPPD.pdf |BayesPPD.html✨
BayesPPD/json (API)
NEWS

# Install 'BayesPPD' in R:

install.packages('BayesPPD', repos = c('https://angieshen6.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

actg019 - AIDS Clinical Trial ACTG019 (1990).
actg036 - AIDS Clinical Trial ACTG036 (1991).

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp openmp

2.48 score 1 packages 7 scripts 403 downloads 9 exports 4 dependencies

Last updated from:a105b5c74c. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	204
linux-devel-x86_64	OK	222
source / vignettes	OK	269
linux-release-arm64	OK	222
linux-release-x86_64	OK	214
macos-release-arm64	OK	244
macos-release-x86_64	OK	390
macos-oldrel-arm64	OK	238
macos-oldrel-x86_64	OK	608
windows-devel	OK	440
windows-release	OK	444
windows-oldrel	OK	472
wasm-release	OK	148

Exports:glm.fixed.a0 glm.random.a0 normalizing.constant power.glm.fixed.a0 power.glm.random.a0 power.two.grp.fixed.a0 power.two.grp.random.a0 two.grp.fixed.a0 two.grp.random.a0

Dependencies:Rcpp RcppArmadillo RcppEigen RcppNumerical

Bayesian Sample Size Determination for Two Group Models (Binary and Normal Outcomes)

Rendered frombayesppd-vignette.Rmdusingknitr::rmarkdownon Apr 05 2026.

Last update: 2022-11-12
Started: 2022-11-12

Citation

Readme and manuals

Help Manual

Help page	Topics
Bayesian sample size determination using the power and normalized power prior for generalized linear models	BayesPPD-package
AIDS Clinical Trial ACTG019 (1990).	actg019
AIDS Clinical Trial ACTG036 (1991).	actg036
Model fitting for generalized linear models with fixed a0	glm.fixed.a0
Model fitting for generalized linear models with random a0	glm.random.a0
Function for approximating the normalizing constant for generalized linear models with random a0	normalizing.constant
Power/type I error calculation for generalized linear models with fixed a0	power.glm.fixed.a0
Power/type I error calculation for generalized linear models with random a0	power.glm.random.a0
Power/type I error calculation for data with two groups (treatment and control group, no covariates) with fixed a0	power.two.grp.fixed.a0
Power/type I error calculation for two groups (treatment and control group, no covariates) with random a0	power.two.grp.random.a0
Model fitting for two groups (treatment and control group, no covariates) with fixed a0	two.grp.fixed.a0
Model fitting for two groups (treatment and control group, no covariates) with random a0	two.grp.random.a0