Easystats

Source: Wikipedia, the free encyclopedia.
Easystats
Initial release2019 (2019)
Written inR
Operating systemAll OS supported by R
Available inEnglish
TypeStatistical software
LicenseGPL-3.0
Websitegithub.com/easystats/easystats

The easystats collection of

R 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][12]

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

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]

The easystats collection of packages as a whole received the 2023 Award from the Society for the Improvement of Psychological Science (SIPS).[13]

Packages

The easystats ecosystem contains ten semi-independent packages.

See also

References

  1. ^ a b "easystats: one year already. What's next?". r-bloggers. 23 January 2020. Retrieved 14 January 2022.
  2. ^ a b c "easystats". GitHub. 14 January 2022. Retrieved 14 January 2022.
  3. ^ "easystats Downloads". GitHub. 14 January 2022. Retrieved 14 January 2022.
  4. ^ "Project "easystats"". ResearchGate. Retrieved 16 January 2022.
  5. ^ "Dominique Makowski's Google Scholar Profile". scholar.google.fr.
  6. ^ "easystats: Quickly investigate model performance". Business Science. 13 July 2021. Retrieved 17 January 2022.
  7. ^ "Automate Textual Reports of Statistical Models in R! report / easystats". YouTube. Retrieved 17 January 2022.
  8. ISBN 978-1446200469.{{cite book}}: CS1 maint: location missing publisher (link
    )
  9. ^ "Analyse des corrélations avec easystats". rzine.fr. Retrieved 17 January 2022.
  10. ^ Su, Gang (2 September 2020). "A Comprehensive List of Handy R Packages". towardsdatascience.com. Retrieved 17 January 2022.
  11. ISBN 9781000353877.{{cite book}}: CS1 maint: location missing publisher (link
    )
  12. ^ Monkman, Martin. "Data Science with R: A Resource Compendium". Retrieved 18 May 2022.
  13. ^ "SIPS 2023 Awards Announced!". improvingpsych. 22 August 2023. Retrieved 29 September 2023.
  14. S2CID 198640623
    .
  15. doi:10.21105/joss.04684
    . Retrieved 29 September 2023.
  16. S2CID 201882316
    .
  17. ^ "SIPS Awards". Retrieved 21 August 2022.
  18. S2CID 225530918
    .
  19. S2CID 233378359
    .
  20. S2CID 229576898
    .
  21. S2CID 225319884
    .
  22. S2CID 238778250
    .
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