Easystats

Last updated
Easystats
Initial release2019 (2019)
Written in R
Operating system All OS supported by R
Available inEnglish
Type Statistical software
License GPL-3.0
Website github.com/easystats/easystats

The easystats collection of open source 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]

Contents

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, Brenton M. Wiernik, Etienne Bacher, and Rémi Thériault. [12]

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

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References

  1. 1 2 "easystats: one year already. What's next?". r-bloggers. 23 January 2020. Retrieved 14 January 2022.
  2. 1 2 "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. 29 November 2021. Retrieved 17 January 2022.
  8. ↑ Field, Andy P. (2012). Discovering statistics using R. Thousand Oaks, California. ISBN   978-1446200469.{{cite book}}: CS1 maint: location missing publisher (link)
  9. ↑ "Analyse des corrélations avec easystats". rzine.fr. Retrieved 17 January 2022.
  10. ↑ Kennedy, Ryan (2021). Introduction to R for social scientists a Tidy programming approach. Boca Raton. ISBN   9781000353877.{{cite book}}: CS1 maint: location missing publisher (link)
  11. ↑ Monkman, Martin. Data Science with R: A Resource Compendium . Retrieved 18 May 2022.
  12. ↑ "easystats Authors". GitHub. 11 November 2024. Retrieved 11 November 2024.
  13. ↑ "SIPS 2023 Awards Announced!". improvingpsych. 22 August 2023. Retrieved 29 September 2023.
  14. ↑ Lüdecke, Daniel; Waggoner, Philip D.; Makowski, Dominique (25 June 2019). "insight: A Unified Interface to Access Information from Model Objects in R". Journal of Open Source Software. 4 (38): 1412. Bibcode:2019JOSS....4.1412L. doi: 10.21105/joss.01412 . S2CID   198640623.
  15. ↑ Patil, Indrajeet; Makowski, Dominique; Ben-Shachar, Mattan S.; Wiernik, Brenton M.; Bacher, Etienne; Lüdecke, Daniel (9 October 2022). "datawizard: An R Package for Easy Data Preparation and Statistical Transformations" (PDF). Journal of Open Source Software. 7 (78): 4684. doi:10.21105/joss.04684 . Retrieved 29 September 2023.
  16. ↑ Makowski, Dominique; Ben-Shachar, Mattan; Lüdecke, Daniel (13 August 2019). "bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework". Journal of Open Source Software. 4 (40): 1541. Bibcode:2019JOSS....4.1541M. doi: 10.21105/joss.01541 . S2CID   201882316.
  17. ↑ "SIPS Awards". 24 July 2018. Retrieved 21 August 2022.
  18. ↑ Makowski, Dominique; Ben-Shachar, Mattan; Patil, Indrajeet; Lüdecke, Daniel (16 July 2020). "Methods and Algorithms for Correlation Analysis in R". Journal of Open Source Software. 5 (51): 2306. Bibcode:2020JOSS....5.2306M. doi: 10.21105/joss.02306 . S2CID   225530918.
  19. ↑ Lüdecke, Daniel; Ben-Shachar, Mattan; Patil, Indrajeet; Waggoner, Philip; Makowski, Dominique (21 April 2021). "performance: An R Package for Assessment, Comparison and Testing of Statistical Models". Journal of Open Source Software. 6 (60): 3139. Bibcode:2021JOSS....6.3139L. doi: 10.21105/joss.03139 . S2CID   233378359.
  20. ↑ Ben-Shachar, Mattan; Lüdecke, Daniel; Makowski, Dominique (23 December 2020). "effectsize: Estimation of Effect Size Indices and Standardized Parameters". Journal of Open Source Software. 5 (56): 2815. Bibcode:2020JOSS....5.2815B. doi: 10.21105/joss.02815 . S2CID   229576898.
  21. ↑ Lüdecke, Daniel; Ben-Shachar, Mattan; Patil, Indrajeet; Makowski, Dominique (9 September 2020). "Extracting, Computing and Exploring the Parameters of Statistical Models using R". Journal of Open Source Software. 5 (53): 2445. Bibcode:2020JOSS....5.2445L. doi: 10.21105/joss.02445 . S2CID   225319884.
  22. ↑ Lüdecke, Daniel; Patil, Indrajeet; Ben-Shachar, Mattan S.; Wiernik, Brenton M.; Waggoner, Philip; Makowski, Dominique (6 August 2021). "see: An R Package for Visualizing Statistical Models". Journal of Open Source Software. 6 (64): 3393. Bibcode:2021JOSS....6.3393L. doi: 10.21105/joss.03393 . S2CID   238778250.
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