Citing the packages, modules and software you used for your analysis is important to acknowledge the time and effort spent by people who create theses tools (sometimes in their free-time, or at the expense of their own research), but also for reproducibility. Indeed, statistical routines are often implemented in different ways by different packages, which can lead to possible discrepancies in the results. Explicitly mentioning that “I did this using this function from that package version 1.2.3” is a way of protecting yourself by being transparent about what you have found doing what you have done.
But, understandably, you must have a lot of questions-
That’s great, but how to actually cite them?
I used about 100 packages, should I cite them all?
How should I report the system (the OS, the R version, etc.)?
We attempt to answer these questions below :)
Ideally, you should indeed cite all the packages that you used. However, it’s often not possible to cite them all in the manuscript body. Therefore, we would recommend the following guidelines:
This should be done for the packages that were central to your specific study (i.e., that got you the results that you reported) rather than data manipulation tools (even though these are as much, if not more, important). For example:
Statistical analysis were carried out using R 4.1.0 (R Core Team, 2021), the rstanarm (v2.13.1; Gabry & Goodrich, 2016) and the report (v0.2.0; Makowski, Patil, & Lüdecke, 2019) packages. The full reproducible code is available in Supplementary Materials.
Then, in Supplementary Materials, you can show all the packages and functions you used. To do it quickly, explicitly and in a reproducible fashion, we recommend writing the Supplementary Materials with R Markdown, which can generate docs and pdf files that you can submit along with your manuscript. Moreover, if you’re using R, you can include (usually at the end) every used package’s citation using the cite_packages() function from the report package. For example:
Finding the right citation information is sometimes complicated. In R, this process is made quite easy, you simply run citation("packagename"). For instance, citation("bayestestR"):
To cite bayestestR in publications use:
Makowski, D., Ben-Shachar, M., \& Lüdecke, D. (2019). bayestestR:
Describing Effects and their Uncertainty, Existence and Significance
within the Bayesian Framework. Journal of Open Source Software,
4(40), 1541. doi:10.21105/joss.01541
A BibTeX entry for LaTeX users is
@Article{,
title = {bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework.},
author = {Dominique Makowski and Mattan S. Ben-Shachar and Daniel Lüdecke},
journal = {Journal of Open Source Software},
doi = {10.21105/joss.01541},
year = {2019},
number = {40},
volume = {4},
pages = {1541},
url = {https://joss.theoj.org/papers/10.21105/joss.01541},
}For other languages, such as Python or Julia, it might be a little trickier, but a quick search on Google (or github) should provide you with all the necessary information (version, authors and date).
Keep in mind that it’s better to have a slightly incomplete citation than no citation at all.
If you want to cite the easystats ecosystem, you can use the cite_easystats() function:
Thanks for crediting us :) You can cite the ‘easystats’ ecosystem as follows:
Data analysis was carried out using the ‘easystats’ collection of packagaes (Ludecke, Waggoner, & Makowski, 2019; Makowski, Ben-Shachar, & Ludecke, 2019; Makowski, Ben-Shachar, Patil, & Ludecke, 2020; Ludecke, Ben-Shachar, Patil, & Makowski, 2020; Ben-Shachar, Ludecke, & Makowski, 2020).
@article{ludecke2019insight, journal = {Journal of Open Source Software}, doi = {10.21105/joss.01412}, issn = {2475-9066}, number = {38}, publisher = {The Open Journal}, title = {insight: A Unified Interface to Access Information from Model Objects in R}, url = {http://dx.doi.org/10.21105/joss.01412}, volume = {4}, author = {L{"u}decke, Daniel and Waggoner, Philip and Makowski, Dominique}, pages = {1412}, date = {2019-06-25}, year = {2019}, month = {6}, day = {25} }
@article{makowski2019bayestestr, title = {{bayestestR}: {Describing} {Effects} and their {Uncertainty}, {Existence} and {Significance} within the {Bayesian} {Framework}}, volume = {4}, issn = {2475-9066}, shorttitle = {{bayestestR}}, url = {https://joss.theoj.org/papers/10.21105/joss.01541}, doi = {10.21105/joss.01541}, number = {40}, urldate = {2019-08-13}, journal = {Journal of Open Source Software}, author = {Makowski, Dominique and Ben-Shachar, Mattan S. and L{"u}decke, Daniel}, month = aug, year = {2019}, pages = {1541} }
@article{makowski2020correlation, doi={10.21105/joss.02306}, title={Methods and Algorithms for Correlation Analysis in R}, author={Makowski, Dominique and Ben-Shachar, Mattan S. and Patil, Indrajeet and L{"u}decke, Daniel}, journal={Journal of Open Source Software}, volume={5}, number={51}, pages={2306}, year={2020} }
@article{ludecke20202parameters, title = {parameters: Extracting, Computing and Exploring the Parameters of Statistical Models using {R}.}, volume = {5}, doi = {10.21105/joss.02445}, number = {53}, journal = {Journal of Open Source Software}, author = {Daniel L{"u}decke and Mattan S. Ben-Shachar and Indrajeet Patil and Dominique Makowski}, year = {2020}, pages = {2445}, }
@article{benchashar2020effectsize, title = {{e}ffectsize: Estimation of Effect Size Indices and Standardized Parameters}, author = {Mattan S. Ben-Shachar and Daniel L{"u}decke and Dominique Makowski}, year = {2020}, journal = {Journal of Open Source Software}, volume = {5}, number = {56}, pages = {2815}, publisher = {The Open Journal}, doi = {10.21105/joss.02815}, url = {https://doi.org/10.21105/joss.02815}, }