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][12]

Easystats
Initial release2019 (2019)
Written inR
Operating systemAll OS supported by R
Available inEnglish
TypeStatistical software
LicenseGPL-3.0
Websitegithub.com/easystats/easystats

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]

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.

  • insight: This package serves as the foundation of the ecosystem as it allows manipulating objects from different R packages.[14]
  • datawizard: This package implements some core data manipulation features.[15]
  • bayestestR: This package provides utilities to work with Bayesian statistics.[16] The package received a Commendation award by the Society for the Improvement of Psychological Science (SIPS) in 2020.[17]
  • correlation: This package is dedicated to running correlation analyses.[18]
  • performance: This package allows the extraction of metrics of model performance.[19]
  • effectsize: This packages computes indices of effect size and standardized parameters.[20]
  • parameters: This package centres around the analysis of the parameters of a statistical model.[21]
  • modelbased: This package computes model-based predictions, group averages and contrasts.
  • see: This package interfaces with ggplot2 to create visual plots.[22]
  • report: This package implements an automated reporting of statistical models.

See also

References
  1. "easystats: one year already. What's next?". r-bloggers. 23 January 2020. Retrieved 14 January 2022.
  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. 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. Su, Gang (2 September 2020). "A Comprehensive List of Handy R Packages". towardsdatascience.com. Retrieved 17 January 2022.
  11. 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)
  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. 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". 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.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.