easystats: An R Framework for Easy Statistical Modeling, Visualization, and Reporting

What is easystats?

easystats is a collection of R packages, which aims to provide a unifying and consistent framework to tame, discipline, and harness the scary R statistics and their pesky models.

However, there is not (yet) an unique “easystats” way of doing data analysis. Instead, start with one package and, when you’ll face a new challenge, do check if there is an easystats answer for it in other packages. You will slowly uncover how using them together facilitates your life. And, who knows, you might even end up using them all.

Installation

Finally, as easystats sometimes depends on some additional packages for specific functions that are not downloaded by default. If you want to benefit from the full easystats experience without any hiccups, simply run the following:

easystats::install_suggested()

Citation

To cite the package, run the following command:

citation("easystats")
To cite easystats in publications use:

  Lüdecke, Patil, Ben-Shachar, Wiernik, Bacher, Thériault, & Makowski
  (2022). easystats: Framework for Easy Statistical Modeling,
  Visualization, and Reporting. CRAN.
  doi:10.32614/CRAN.package.easystats
  <https://doi.org/10.32614/CRAN.package.easystats>

A BibTeX entry for LaTeX users is

  @Article{,
    title = {easystats: Framework for Easy Statistical Modeling, Visualization, and Reporting},
    author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Brenton M. Wiernik and Etienne Bacher and Rémi Thériault and Dominique Makowski},
    journal = {CRAN},
    doi = {10.32614/CRAN.package.easystats},
    year = {2022},
    note = {R package},
    url = {https://easystats.github.io/easystats/},
  }

If you want to do this only for certain packages in the ecosystem, have a look at this article on how you can do so! https://easystats.github.io/easystats/articles/citation.html

Getting started

Each easystats package has a different scope and purpose. This means your best way to start is to explore and pick the one(s) that you feel might be useful to you. However, as they are built with a “bigger picture” in mind, you will realize that using more of them creates a smooth workflow, as these packages are meant to work together. Ideally, these packages work in unison to cover all aspects of statistical analysis and data visualization.

• report: