New paper out in JOSS:
“ArviZ: a modular and flexible library for exploratory analysis of Bayesian models”
It covers the design principles behind ArviZ and the motivation for the refactoring in 1.0. https://joss.theoj.org/papers/10.21105/joss.09889
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@ArviZ@bayes.club
ArviZ 1.0 features new state-of-the-art diagnostics, improved plotting syntax, and visualizations. Explore the docs and examples, or stay tuned for upcoming threads! https://python.arviz.org/projects/plots/en/latest/gallery/index.html
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@ArviZ@bayes.club
📦 arviz-plots has no plotting library as a dependency, but it can generate plots with matplotlib, bokeh, and/or plotly if they are installed. See arviz-plots.readthedocs.io for details
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@ArviZ@bayes.club
📦 arviz-stats can be used with a minimal array API (NumPy + SciPy). Ideal for PPL devs who want ArviZ diagnostics without extra deps. Or you can use the xarray API with all its bells and whistles, which is ideal for end users interested in data analysis. See arviz-stats.readthedocs.io for details.
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@ArviZ@bayes.club
📦 arviz-base has no I/O library as a dependency, but you can use netcdf4, h5netcdf, or zarr to read and write your data. Install only the one you need. See arviz-base.readthedocs.io for details.
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@ArviZ@bayes.club
You can keep installing and importing ArviZ as usual, but if needed, you can easily get more control. Functionality is now split into 3 libraries: 📦 arviz-base 📦 arviz-stats 📦 arviz-plots.
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@ArviZ@bayes.club
InferenceData has been replaced with xarray’s DataTree, a more flexible data structure that will still feel familiar to you https://python.arviz.org/en/stable/user_guide/migration_guide.html#datatree
ArviZ 1.0 is out! We have refactored it to be more modular, flexible & lightweight. For an overview of the changes, check the migration guide. https://python.arviz.org/en/stable/user_guide/getting_started.html
ArviZ is participating in Google Summer of Code 2024 under the #NumFOCUS umbrella. We have projects in both #Python and #JuliaLang. If interested in working on one of these projects, get in touch! https://github.com/arviz-devs/arviz/wiki/GSoC-2024-projects @TuringLang@bayes.club #GSoC #JSoC #bayesian
Our @joss@fosstodon.org paper on PreliZ was just published! Powered by ArviZ, PreliZ is a one-stop shop for prior elicitation in Python. https://doi.org/10.21105/joss.05499
We've put together a survey https://forms.gle/Wor6iHnp4Ebdg6R36 to gather feedback and insights from our users on a potential dashboard feature for Python ArviZ. Your input would be greatly valued!