Easystats

Easystats

Initial release	2019 (2019)
Written in	R
Operating system	All OS supported by R
Available in	English
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]}

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.

• 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. ^[22]
• see: This package interfaces with ggplot2 to create visual plots. ^[23]
• report: This package implements an automated reporting of statistical models.

See also

• Tidyverse
• R (programming language)

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. 29 November 2021. Retrieved 17 January 2022.
8. Field, Andy P. (2012). Discovering statistics using R. Thousand Oaks, California. ISBN   978-1446200469.
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.
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. Bibcode:2022JOSS....7.4684P. 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. Makowski, Dominique; Ben-Shachar, Mattan S.; Wiernik, Brenton M.; Patil, Indrajeet; Thériault, Rémi; Lüdecke, Daniel (30 May 2025). "modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions". Journal of Open Source Software. 10 (109): 7969. doi:10.21105/joss.07969.
23. 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.