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predictionInterval  

Prediction Interval Functions for Assessing Replication Study Results
View on CRAN: Click here


Download and install predictionInterval package within the R console
Install from CRAN:
install.packages("predictionInterval")

Install from Github:
library("remotes")
install_github("cran/predictionInterval")

Install by package version:
library("remotes")
install_version("predictionInterval", "1.0.0")



Attach the package and use:
library("predictionInterval")
Maintained by
David Stanley
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-08-20
Latest Update: 2016-08-20
Description:
A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the original study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a prediction interval). This package has functions that calculate the prediction interval for the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean.
How to cite:
David Stanley (2016). predictionInterval: Prediction Interval Functions for Assessing Replication Study Results. R package version 1.0.0, https://cran.r-project.org/web/packages/predictionInterval. Accessed 26 Oct. 2026.
Previous versions and publish date:
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Complete documentation for predictionInterval
Functions, R codes and Examples using the predictionInterval R package
Some associated functions: pi.d.demo . pi.d . pi.m.demo . pi.m . pi.r.demo . pi.r . predictionInterval-package . 
Some associated R codes: correlationFunctions.R . dValueFunctions.R . meanFunctions.R . predictionInterval.R . utilityFunctions.R .  Full predictionInterval package functions and examples
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