The goal of BayesianPower is to determine the required sample size or the unconditional and conditional error probabilities when a hypothesis that imposes inequality constraints between group means is compared with its complement, another inequality constraint hypothesis or the unconstrained hypothesis. Klaassen, Hoijtink & Gu (unpublished) describe four methods of weighing unconditional or conditional error probabilities to determine the sample size. Reversely, the same rules can be applied to determine the power of a study.
You can install the released version of BayesianPower from CRAN with:
install.packages("BayesianPower")
And the development version from GitHub with:
# install.packages("devtools")
# devtools::install_github("fayetteklaassen/BayesianPower")
install.packages("BayesianPower", repos = c('https://fayetteklaassen.r-universe.dev', 'https://cloud.r-project.org'))
There is a vignette available on GitHub that contains a description of the functionality of the package and some examples.