In fayetteklaassen/BayesianPower: Sample Size and Power for Comparing Inequality Constrained Hypotheses

knitr::opts_chunk$set(
  collapse = TRUE,
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BayesianPower

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 Klaassen, Hoijtink & Gu (unpublished) describe four methods of weighing unconditional or conditional error probabilities to determine the sample size.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.

Installation

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")

Vignette

There is a vignette available on GitHub that contains a description of the functionality of the package and some examples.

fayetteklaassen/BayesianPower documentation built on June 26, 2020, 10:03 p.m.