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ppRep

ppRep is an R package for Bayesian estimation and testing of effect sizes based on original and replication study using a power prior framework for dynamic discounting of the original data. For more information, see the paper

Pawel, S., Aust, F., Held, L., and Wagenmakers, E.-J. (2023). Power priors for replication studies. TEST. doi:10.1007/s11749-023-00888-5

Installation

## CRAN version
install.packages("ppRep")

## from GitHub
## install.packages("remotes") # requires remotes package
remotes::install_github(repo = "SamCH93/ppRep")

Usage

library("ppRep")

## data from one replication of "Labels" experiment in Protzko et al. (2020)
to <- 0.2 # original SMD effect estimate
so <- 0.05 # original standard error
tr <- 0.09 # replication SMD effect estimate
sr <- 0.05 # replication standard error

## compute and plot posterior density with 95% HPD credible intervals
plotPP(tr = tr, sr = sr, to = to, so = so, CI = TRUE)

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Install

install.packages('ppRep')

Monthly Downloads

129

Version

0.42.3

License

GPL-3

Issues

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Maintainer

Samuel Pawel

Last Published

October 19th, 2023

Functions in ppRep (0.42.3)

postNormMean

Mean of normalized power prior
bfPPalpha

Bayes factor for testing power parameter
margLik

Marginal likelihood of replication effect estimate
bfPPtheta

Bayes factor for testing effect size
postPP

Posterior density of effect size and power parameter
postPPalpha

Marginal posterior distribution of power parameter
postPPtheta

Marginal posterior distribution of effect size
plotPP

Plot joint and marginal posterior distributions
postNormVar

Variance of normalized power prior