flipr 0.2.1

A new version of the flipr package has been released! Check it out if you are looking for a consistent, robust and easy way of doing permutation inference.


Department of Mathematics Jean Leray, UMR CNRS 6629


April 6, 2021

The permutation framework is particularly well suited for inferential purposes as it allows one to do point estimation, confidence regions and hypothesis tests. The flipr package makes it easy and fun to perform all these inferential tasks within the permutation paradigm.

The central object is the so-called \(p\)-value function. The \(p\)-value function for a set of parameters \(\Theta\) is a curve that represents the variation of the \(p\)-value of an hypothesis test in which the null hypothesis is \(\Theta = \Theta_0\) as a function of \(\Theta_0\) (Martin 2017; Fraser 2019; Infanger and Schmidt-Trucksäss 2019).

Observe that, the non-parametric combination method available in the permutation framework (Pesarin and Salmaso 2010) allows you to infer multiple parameters at once. This means that you can natively compute a single confidence region for multiple parameters that controls the family-wise error rate by construction (think of a confidence region for both the mean and the variance for instance).

Version 0.2.1 of the flipr package released on CRAN features \(4\) main functions:

This version also implements a number of small changes and bug fixes, the full list of which is available here.

Finally, a number of articles can be found on the dedicated website for flipr: https://astamm.github.io/flipr/. They explain in details how the permutation framework can be used for statistical inference and how flipr offers easy tools for making that happen seamlessly.


Fraser, D. A. S. 2019. “The p-Value Function and Statistical Inference.” The American Statistician 73 (sup1): 135–47. https://doi.org/10.1080/00031305.2018.1556735.
Infanger, Denis, and Arno Schmidt-Trucksäss. 2019. “P Value Functions: An Underused Method to Present Research Results and to Promote Quantitative Reasoning.” Statistics in Medicine 38 (21): 4189–97. https://doi.org/10.1002/sim.8293.
Martin, Ryan. 2017. “A Statistical Inference Course Based on p-Values.” The American Statistician 71 (2): 128–36. https://doi.org/10.1080/00031305.2016.1208629.
Pesarin, Fortunato, and Luigi Salmaso. 2010. “Permutation Tests for Complex Data,” March. https://doi.org/10.1002/9780470689516.