Package: BayesPPD 1.1.2
Yueqi Shen
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>.
Authors:
BayesPPD_1.1.2.tar.gz
BayesPPD_1.1.2.tar.gz(r-4.5-noble)BayesPPD_1.1.2.tar.gz(r-4.4-noble)
BayesPPD_1.1.2.tgz(r-4.4-emscripten)BayesPPD_1.1.2.tgz(r-4.3-emscripten)
BayesPPD.pdf |BayesPPD.html✨
BayesPPD/json (API)
NEWS
# Install 'BayesPPD' in R: |
install.packages('BayesPPD', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:e7331ed92d. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-linux-x86_64 | OK | Oct 31 2024 |
Exports:glm.fixed.a0glm.random.a0normalizing.constantpower.glm.fixed.a0power.glm.random.a0power.two.grp.fixed.a0power.two.grp.random.a0two.grp.fixed.a0two.grp.random.a0
Dependencies:RcppRcppArmadilloRcppEigenRcppNumerical