Repeatability Estimation for Gaussian and Non-Gaussian Data

Estimating repeatability (intra-class correlation) from Gaussian, binary, proportion and Poisson data.

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rptR provides a collection of functions for calculating point estimates, confidence intervals and significance tests of the repeatability (intra-class correlation coefficient) of measurements, as well as on the variances themselves. The function rpt is a the core functions that calls more specialised functions as required. Specialised functions can also be called directly (see ?rpt for details). All functions return lists of values. The function ?summary.rpt produces summaries in a detailed format, whereby ?plot.rpt plots the distributions of bootstrap or permutation test estimates.

  • get the latest development version from github with
    # building vignettes might take some time. Set build_vignettes = FALSE for a quick download.
    devtools::install_github("mastoffel/rptR", build_vignettes = TRUE)
    # tutorial


Stoffel, M. A., Nakagawa, S. and Schielzeth, H. (2017), rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol Evol. 8: 1639-1644.



rptR 0.9.1


  • repeatabilities for random-slope models

  • update argument to update bootstraps and permutations

  • Progress bars for all non-parallel functions

  • several new sections in documentation and vignette

rptR 0.9.2

  • stability improvements for random-slope models

  • binary (0/1) data is now fitted without overdispersion

  • citation("rptR") now shows the accepted paper citation

  • slight improvements to the Likelihood-ratio tests

rptR 0.9.21

  • bug fix in variance addition in rptPoisson

  • full citation added

rptR 0.9.22

Reference manual

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0.9.22 by Martin Stoffel, 3 years ago

Browse source code at

Authors: Martin Stoffel <[email protected]> , Shinichi Nakagawa <[email protected]> , Holger Schielzeth <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Imports methods, stats, lme4, parallel, pbapply

Suggests testthat, knitr, rmarkdown

Imported by aniDom.

See at CRAN