Roadmap

Short term (next one or two releases)

• Consolidate ArviZ integration

  • Document new functionality
  • Support new functionality (including loo-related diagnostics)

• Revisit default priors see #230

• Improve documentation

• Clean and update code, specially to remove inconsistencies or incomplete code

• Revisit tests.

• Work on porting code from books

  • Regression and other stories
  • Statistical Rethinking

Long term (~ next year)

• Think about long-term features
  • INLA support?
  • Splines
  • Gaussian processes
  • PyMC4 support
  • ?

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Ideas for next release

1. New features

1. Allow "R-side" covariance structures (#110) and covariance priors in general (for varying effects too)
2. Implement scikit-learn compatibility (#105) -> a .predict() method.
3. Add support for splines (#214)
4. Add support for Gaussian processes (#215)
5. save_model and load_model (#259)
6. Fromula in priors (#174)
7. Add option to plot model specification (#287)

2. Fixes

1. Bambi fails when p > n (#278)

3. Documentation

1. Add example of posterior predictive sampling (and or check) (#252)
2. Add example of prior predictive sampling (and or check) (#251)

4. Tests

We added tests during the last release, but we haven't revisited existing tests.

1. Revisit and expand tests in general.

5. Some things I think will give us more power and ease things in the long-term.

1. Allow specification of non-builtin backend (#201)
2. Decrease our dependency on statsmodels.