Bambi

Latest version: v0.14.0

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0.14.0

New features

* Add configuration facilities to Bambi (745)
* Interpet submodule now outputs informative messages when computing default values (745)
* Bambi supports weighted responses (761)
* Bambi supports constrained responses (764)
* Implement `compute_log_likelihood()` method to compute the log likelihood on a model (769)
* Add a class `InferenceMethods` that allows users to access the available inference methods and kwargs (795)

Maintenance and fixes

* Fix bug in predictions with models using HSGP (780)
* Fix `get_model_covariates()` utility function (801)
* Upgrade PyMC dependency to >= 5.13 (803)
* Use `pm.compute_deterministics()` to compute deterministics when bayeux based samplers are used (803)
* Wrap all the parameters of the response distribution (the likelihood) with a `pm.Deterministic` (804)
* Keep `bayeux-ml` as the single direct JAX-related dependency (804)
* The response component only holds response information about the response, not about predictors of the parent parameter (804)
* Resolve import error associated with bayeux (822)

Documentation

* Our Code of Conduct now includes how to send a report (783)
* Add polynomial regression example (809)
* Add Contact form to our webpage (816)

Deprecation

* `f"{response_name}_obs"` has been replaced by `"__obs__"` as the dimension name for the observation index (804)
* `f"{response_name}_{parameter_name}"` is no longer the name for the name of parameters of the likelihood. Now Bambi uses `"{parameter_name}"` (804)
* `kind` in `Model.predict()` now use `"response_params"` and `"response"` instead of `"mean"` and `"pps"` (804)
* `include_mean` has been replaced by `include_response_params` in `Model.fit()` (804)

0.13.0

This is the first version of Bambi that is released with a Governance structure. Added in 709.

New features

* Bambi now supports censored responses (697)
* Implement `"exponential"` and `"weibull"` families (697)
* Add `"kidney"` dataset (697)
* Add `interpret` submodule (684, 695, 699, 701, 732, 736)
* Implements `comparisons`, `predictions`, `slopes`, `plot_comparisons`, `plot_predictions`, and `plot_slopes`
* Support censored families

Maintenance and fixes

* Bump `quartodoc` version to 0.6.1 (720)
* Replace `univariate_ordered` with `ordered` (724)
* Add missing docstring for `center_predictors` (726)
* Fix bugs in `plot_comparison` (731)

Documentation

* Add docstrings to utility functions (696)
* Migrate documentation to Quarto (712)
* Add case study for MRP (716)
* Add example about ordinal regression (719)
* Add example about zero inflated models (725)
* Add example about predictions for new groups (734)

Deprecation

* Drop official suport for Python 3.8 (720)
* Change `plots` submodule name to `interpret` (705)

0.12.0

New features

* Implement new families `"ordinal"` and `"sratio"` for modeling of ordinal responses (678)
* Allow families to implement a custom `create_extra_pps_coord()` (688)
* Allow predictions on new groups (693)

Maintenance and fixes

* Robustify how Bambi handles dims (682)
* Fix links in FAQ (686)
* Update additional dependencies install command (689)
* Update predict pps docstring (690)
* Add warning for aliases athat aren't used (691)

Documentation

* Add families to the Getting Started guide (683)

Deprecation

0.11.0

New features

* Add support for Gaussian Processes via the HSGP approximation (632)
* Add new families: `"zero_inflated_poisson"`, `"zero_inflated_binomial"`, and `"zero_inflated_negativebinomial"` (654)
* Add new families: `"beta_binomial"` and `"dirichlet_multinomial"` (659)
* Allow `plot_cap()` to show predictions at the observation level (668)
* Add new families: `"hurdle_gamma"`, `"hurdle_lognormal"`, `"hurdle_negativebinomial"`, and `"hurdle_poisson"` (676)

Maintenance and fixes

* Modify how HSGP is built in PyMC when there are groups (661)
* Modify how Bambi is imported in the tests (662)
* Prevent underscores from being removed in dim names (664)
* Bump sphix dependency to a version greater than 7 (672)

Documentation

* Document how to use custom priors (656)
* Fix name of arviz traceplot function in the docs (666)
* Add example that shows how `plot_cap()` works (670)

Deprecation

0.10.0

New features

* Refactored the codebase to support distributional models (607)
* Added a default method to handle posterior predictive sampling for custom families (625)
* `plot_cap()` gains a new argument `target` that allows to plot different parameters of the response distribution (627)

Maintenance and fixes

* Moved the `tests` directory to the root of the repository (607)
* Don't pass `dims` to the response of the likelihood distribution anymore (629)
* Remove requirements.txt and replace with `pyproject.toml` config file to distribute the package (631)

Documentation

* Update examples to work with the new internals (607)
* Fixed figure in the Sleepstudy example (607)
* Add example using distributional models (641)

Deprecation

* Removed versioned documentation webpage (616)
* Removed correlated priors for group-specific terms (607)
* Dictionary with tuple keys are not allowed for priors anymore (607)

0.9.3

Maintenance and fixes

* Update to PyMC >= 5, which means we use PyTensor instead of Aesara now (613, 614)

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