The easystats collection of open sourceR packages was created in 2019 and primarily includes tools dedicated to the post-processing of statistical models.[1][2] As of May 2022, the 10 packages composing the easystats ecosystem have been downloaded more than 8 million times, and have been used in more than 1000 scientific publications.[3][4][5] The ecosystem is the topic of several statistical courses, video tutorials and books.[6][7][8][9][10][11]
The aim of easystats is to provide a unifying and consistent framework to understand and report statistical results. It is also compatible with other collections of packages, such as the tidyverse. Notable design characteristics include its API, with a particular attention given to the names of functions and arguments (e.g., avoiding acronyms and abbreviations), and its low number of dependencies.[2][better source needed]
History
In 2019, Dominique Makowski contacted software developer Daniel Lüdecke with the idea to collaborate around a collection of R packages aiming at facilitating data science for users without a statistical or computer science background. The first package of easystats, insight was created in 2019, and was envisioned as the foundation of the ecosystem.[1] The second package that emerged, bayestestR, benefitted from the joining of Bayesian expert Mattan S. Ben-Shachar. Other maintainers include Indrajeet Patil and Brenton M. Wiernik.[2]
^Field, Andy P. (2012). Discovering statistics using R. Thousand Oaks, California. ISBN978-1446200469.{{cite book}}: CS1 maint: location missing publisher (link)
^Kennedy, Ryan (2021). Introduction to R for social scientists a Tidy programming approach. Boca Raton. ISBN9781000353877.{{cite book}}: CS1 maint: location missing publisher (link)