References

[1]

M. Perugini, M. Gallucci, G. Costantini, Safeguard power as a protection against imprecise power estimates, Perspectives on Psychological Science 9 (2014) 319–332. https://doi.org/10.1177/1745691614528519.

[2]

E. Clarke, S. Sherrill-Mix, Ggbeeswarm: Categorical scatter (violin point) plots, 2017. https://CRAN.R-project.org/package=ggbeeswarm.

[3]

Y. Xie, Dynamic documents with R and knitr, 2nd ed., Chapman; Hall/CRC, Boca Raton, Florida, 2015. http://yihui.org/knitr/.

[4]

M.F.W. Festing, Randomized Block Experimental Designs Can Increase the Power and Reproducibility of Laboratory Animal Experiments, ILAR Journal 55 (2014) 472–476. https://doi.org/10.1093/ilar/ilu045.

[5]

M.S. Ben-Shachar, D. Lüdecke, D. Makowski, effectsize: Estimation of effect size indices and standardized parameters, Journal of Open Source Software 5 (2020) 2815. https://doi.org/10.21105/joss.02815.

[6]

D. Lüdecke, M.S. Ben-Shachar, I. Patil, D. Makowski, Parameters: Extracting, computing and exploring the parameters of statistical models using R., Journal of Open Source Software 5 (2020) 2445. https://doi.org/10.21105/joss.02445.

[7]

P. Green, C.J. MacLeod, Simr: An r package for power analysis of generalised linear mixed models by simulation, Methods in Ecology and Evolution 7 (2016) 493–498. https://doi.org/10.1111/2041-210X.12504.

[8]

C.O. Wilke, Cowplot: Streamlined plot theme and plot annotations for ’ggplot2’, 2020. https://CRAN.R-project.org/package=cowplot.

[9]

S. Champely, Pwr: Basic functions for power analysis, 2020. https://CRAN.R-project.org/package=pwr.

[10]

Y. Xie, Bookdown: Authoring books and technical documents with r markdown, 2020. https://github.com/rstudio/bookdown.

[11]

J. Fox, S. Weisberg, B. Price, Car: Companion to applied regression, 2020. https://CRAN.R-project.org/package=car.

[12]

R. Lenth, Emmeans: Estimated marginal means, aka least-squares means, 2020. https://github.com/rvlenth/emmeans.

[13]

H. Wickham, W. Chang, L. Henry, T.L. Pedersen, K. Takahashi, C. Wilke, K. Woo, H. Yutani, D. Dunnington, ggplot2: Create elegant data visualisations using the grammar of graphics, 2020. https://CRAN.R-project.org/package=ggplot2.

[14]

K. Goode, K. Rey, ggResidpanel: Panels and interactive versions of diagnostic plots using ggplot2, 2019. https://goodekat.github.io/ggResidpanel/.

[15]

D. Bates, M. Maechler, B. Bolker, S. Walker, lme4: Linear mixed-effects models using eigen and S4, 2020. https://github.com/lme4/lme4/.

[16]

A. Kuznetsova, P. Bruun Brockhoff, R. Haubo Bojesen Christensen, lmerTest: Tests in linear mixed effects models, 2020. https://github.com/runehaubo/lmerTestR.

[17]

H. Wickham, Tidyr: Tidy messy data, 2020. https://CRAN.R-project.org/package=tidyr.

[18]

A.C. Davison, D.V. Hinkley, Bootstrap methods and their applications, Cambridge University Press, Cambridge, 1997. http://statwww.epfl.ch/davison/BMA/.