Pybads

What's changed
--------------------------
* Official JOSS version of PyBADS
* Add checks on input bounds and change message errors
* New descriptions of the documentation

Full change log: [v1.0.3...v1.0.4](https://github.com/acerbilab/pybads/compare/v1.0.3...v1.0.4)

What's changed
--------------------------
* Add checks and messages logs for the transformed gridized initial point and non-box constraints violations

Full change log: [v1.0.2...v1.0.3](https://github.com/acerbilab/pybads/compare/v1.0.2...v1.0.3)

What's changed
--------------------------
* Minor fixes for pytest warnings and fixed minor bug for 1D optimization
* Moved optional dependencies to dev env

Full change log: [v1.0.1...v1.0.2](https://github.com/acerbilab/pybads/compare/v1.0.1...v1.0.2)

- Fix bug wrong reshape when broadcasting a matrix operation for high-dimension problems ( $D > 32$) during the evolutionary search.

**First full-version release of PyBADS**, a Python package for fast and robust *black-box* optimization. Full documentation is available at https://acerbilab.github.io/pybads/. Feedback is welcome, see troubleshooting and contact. The same packaged version is also available at https://pypi.org/project/PyBADS/#history.
Additional detail of the algorithm can be found the paper Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search paper published in NeurIPS in [2017](https://papers.nips.cc/paper_files/paper/2017/hash/df0aab058ce179e4f7ab135ed4e641a9-Abstract.html).

A MATLAB implementation is also available at the [acerbilab/BADS](https://github.com/acerbilab/bads) repository.

Initial public beta version of PyBADS, a Python package for solving difficult and moderately expensive optimization problems. Full documentation is available at [acerbilab.github.io/pybads/](https://acerbilab.github.io/pybads/). Feedback is welcome, see [troubleshooting and contact](https://github.com/acerbilab/pybads#troubleshooting-and-contact). The same packaged version is also available at [pypi](https://pypi.org/project/PyBADS/#history).
Additional details of the algorithm can be found in the original Bayesian Adaptive Direct Search paper presented at [NeurIPS in 2017](https://papers.nips.cc/paper/2017/hash/df0aab058ce179e4f7ab135ed4e641a9-Abstract.html).
A MATLAB implementation is also available at the [acerbilab/BADS](https://github.com/acerbilab/bads) repository.