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Easystats
Software package for the R language From Wikipedia, the free encyclopedia
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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]
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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]
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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.
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See also
References
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