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jarrodmillman
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stefanv
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Jun 11, 2025
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This is a good overview, thanks!
The one thing I'm missing is an overview of the audience / target groups for our effort. E.g., here's a short section I wrote for another purpose:
We help:
- Educators teach statistics using a comprehensive, free, computational ecosystem with clear user interfaces.
- Researchers produce correct results, via an extensive collection of well engineered and tested computational libraries, with intuitive APIs and good documentation.
- Method developers ship their innovations easily to a wide audience.
- Practicing staticians & data scientists access powerful tools to compute results efficiently, without the friction of switching ecosystems.
We foster a sustainable ecosystem, aiming to attract statisticians who actively engage with development of the tools they use daily.
| Libraries like scikit-learn are especially valued for their clean, consistent interfaces and their integration with the broader Python data stack, which streamlines workflows and enhances usability for both new and experienced users. | ||
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| While there are many smaller, specialized packages available, the ecosystem remains dominated by these large, general-purpose libraries. This concentration of resources ensures stability and quality but can also limit the visibility and adoption of innovative or niche statistical tools. | ||
| As Python’s role in statistics continues to grow, fostering a more diverse and accessible ecosystem will be key to meeting the evolving needs of educators, researchers, and practitioners. |
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It will also involve increasing developer participation in the core packages.
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| # Relationship to Other Languages | ||
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| R remains the gold standard for statistics, with better branding, a more cohesive ecosystem, and more teaching resources. |
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And they have contributions from the methods developers.
Co-authored-by: Stefan van der Walt <stefan@mentat.za.net>
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