Following multiple improvements and being actively used in multiple projects, the BayesFlow library is ready to move beyond the beta phase!

Features:

1. Added option for permutation='learnable' when creating an InvertibleNetwork
2. Added option for coupling_design in ["affine", "spline", "interleaved"] when creating an InvertibleNetwork
3. Simplified passing additional settings to the internal networks. For instance, you
   can now simply do
   inference_network = InvertibleNetwork(num_params=20, coupling_net_settings={'mc_dropout': True})
   to get a Bayesian neural network.
4. PMPNetwork has been added for model comparison according to findings in https://arxiv.org/abs/2301.11873
5. Publication-ready calibration diagnostic for expected calibration error (ECE) in a model comparison setting has been
   added to diagnostics.py and is accessible as plot_calibration_curves()
6. A new module experimental has been added currently containing rectifiers.py.
7. Default settings for transformer-based architectures.
8. Numerical calibration error using posterior_calibration_error()

General Improvements:

1. Improved docstrings and consistent use of keyword arguments vs. configuration dictionaries
2. Increased focus on transformer-based architectures as summary networks
3. Figures resulting diagnostics.py have been improved and prettified
4. Added a module sensitivity.py for testing the sensitivity of neural approximators to model misspecification
5. Multiple bugfixes, including a major bug affecting the saving and loading of learnable permutations

The project now also features automatic PyPI publishing. :)