Initial release | 2019 |
---|---|
Written in | R |
Operating system | All OS supported by R |
Available in | English |
Type | Statistical software |
License | GPL-3.0 |
Website |
github |
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]
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]
The easystats ecosystem contains ten semi-independent packages.
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link)
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cite book}}
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link)
Initial release | 2019 |
---|---|
Written in | R |
Operating system | All OS supported by R |
Available in | English |
Type | Statistical software |
License | GPL-3.0 |
Website |
github |
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]
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]
The easystats ecosystem contains ten semi-independent packages.
{{
cite book}}
: CS1 maint: location missing publisher (
link)
{{
cite book}}
: CS1 maint: location missing publisher (
link)