Software:Easystats

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Short description: Software package for the R language
Easystats
Easystats logo.png
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 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]

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]

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. ↑ 1.0 1.1 "easystats: one year already. What's next?". 23 January 2020. https://www.r-bloggers.com/2020/01/easystats-one-year-already-whats-next/. 
  2. ↑ 2.0 2.1 2.2 "easystats". 14 January 2022. https://github.com/easystats/easystats. 
  3. ↑ "easystats Downloads". 14 January 2022. https://github.com/easystats/easystats#downloads. 
  4. ↑ "Project "easystats"". https://www.researchgate.net/project/Project-easystats-making-statistical-computations-with-R-easier. 
  5. ↑ "Dominique Makowski's Google Scholar Profile". https://scholar.google.fr/citations?user=bg0BZ-QAAAAJ&hl=en. 
  6. ↑ "easystats: Quickly investigate model performance" (in en). 13 July 2021. https://www.business-science.io/r/2021/07/13/easystats-performance-check-model.html. 
  7. ↑ "Automate Textual Reports of Statistical Models in R! report / easystats" (in en). https://www.youtube.com/watch?v=_ypkrGyqyZ4. 
  8. ↑ Field, Andy P. (2012). Discovering statistics using R. Thousand Oaks, California. ISBN 978-1446200469. 
  9. ↑ "Analyse des corrélations avec easystats". https://rzine.fr/docs/20200526_glecampion_initiation_aux_correlations/index.html. 
  10. ↑ Su, Gang (2 September 2020). "A Comprehensive List of Handy R Packages" (in en). https://towardsdatascience.com/a-comprehensive-list-of-handy-r-packages-e85dad294b3d. 
  11. ↑ Kennedy, Ryan (2021). Introduction to R for social scientists a Tidy programming approach. Boca Raton. ISBN 9781000353877. 
  12. ↑ Monkman, Martin. "Data Science with R: A Resource Compendium". https://bookdown.org/martin_monkman/DataScienceResources_book/. 
  13. ↑ "SIPS 2023 Awards Announced!". 22 August 2023. https://improvingpsych.org/2023/08/22/sips-2023-awards-announced/. 
  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. doi:10.21105/joss.01412. Bibcode: 2019JOSS....4.1412L. 
  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". Journal of Open Source Software 7 (78): 4684. doi:10.21105/joss.04684. https://joss.theoj.org/papers/10.21105/joss.04684.pdf. 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. doi:10.21105/joss.01541. Bibcode: 2019JOSS....4.1541M. 
  17. ↑ "SIPS Awards". https://improvingpsych.org/mission/awards/. 
  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. doi:10.21105/joss.02306. Bibcode: 2020JOSS....5.2306M. 
  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. doi:10.21105/joss.03139. Bibcode: 2021JOSS....6.3139L. 
  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. doi:10.21105/joss.02815. Bibcode: 2020JOSS....5.2815B. 
  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. doi:10.21105/joss.02445. Bibcode: 2020JOSS....5.2445L. 
  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. doi:10.21105/joss.03393. Bibcode: 2021JOSS....6.3393L. 



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