Skip to main content

How to Cite Packages

(This article was first published on Dominique Makowski, and kindly contributed to R-bloggers)

Citing the packages, modules and softwares you used for your analysis is important, both from a reproducibility perspective (statistical routines are often implemented in different ways by different packages, which could explain slight discrepancies in the results. Saying "I did this using this function from that package version 1.2.3� is a way of protecting yourself by being clear about what you have found doing what you have done) but also for acknowledging the work and time that people spent creating tools for others (sometimes at the expense of their own research).

  • That's great, but how to actually cite them?
  • I used about 100 packages, should I cite them all?

What should I cite?

Ideally, you should indeed cite all the packages that you used. However, it's not very diegetic. Therefore, I would recommand the following:

  1. Cite the main / important packages in the manuscript

This should be done for the packages that were central to your specific analysis (i.e., that got you the results that you reported) rather than data manipulation tools (even though these are as much important).

For example:

Statistics were done using R 3.5.0 (R Core Team, 2018), the rstanarm (v2.13.1; Gabry & Goodrich, 2016) and the psycho (v0.3.4; Makowski, 2018) packages. The full reproducible code is available in Supplementary Materials.

  1. Present everything in Supplementary Materials

Then, in Supplementary Materials, you show the packages and functions you used. Moreover, in R, you can include (usually at the end) every used package and their version using the sessionInfo() function.

How should I cite it?

Finding the right citation information is sometimes complicated. In R, this process is made quite easy, you simply run citation("packagename"). For instance, citation("dplyr"):

To cite ‘dplyr' in publications use:

  Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2018). dplyr: A Grammar of Data Manipulation. R package version
  0.7.6. https://CRAN.R-project.org/package=dplyr

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {dplyr: A Grammar of Data Manipulation},
    author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
    year = {2018},
    note = {R package version 0.7.6},
    url = {https://CRAN.R-project.org/package=dplyr},
  }

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, date). It's better to have a slightly incomplete citation than no citation at all.

Previous blogposts

To leave a comment for the author, please follow the link and comment on their blog: Dominique Makowski.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...


from R-bloggers https://ift.tt/2PPmUZY
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Solving Van der Pol equation with ivp_solve

Van der Pol’s differential equation is The equation describes a system with nonlinear damping, the degree of damping given by μ. If μ = 0 the system is linear and undamped, but for positive μ the system is nonlinear and damped. We will plot the phase portrait for the solution to Van der Pol’s equation in Python using SciPy’s new ODE solver ivp_solve . The function ivp_solve does not solve second-order systems of equations directly. It solves systems of first-order equations, but a second-order differential equation can be recast as a pair of first-order equations by introducing the first derivative as a new variable. Since y is the derivative of x , the phase portrait is just the plot of ( x , y ). If μ = 0, we have a simple harmonic oscillator and the phase portrait is simply a circle. For larger values of μ the solutions enter limiting cycles, but the cycles are more complicated than just circles. Here’s the Python code that made the plot. from scipy import linspace from ...
Post a Comment
Read more

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...
Post a Comment
Read more