This release features two quality of life improvements: a better lifting function interface for use outside of `scikit-learn`, and improved trajectory prediction functionality. More importantly, [the docs have been reorganized](https://pykoop.readthedocs.io/en/stable/), the unit tests are no longer a mess, and [some Jupyter notebook examples have been added to binder](https://mybinder.org/v2/gh/decarsg/pykoop/main?labpath=notebooks).
Full changelog: https://github.com/decarsg/pykoop/compare/v1.0.4...v1.0.5
New features
* Added `lift()`, `lift_state()`, `lift_input()`, `retract()`, `retract_state()`, and `retract_input()` helper methods to `KoopmanPipeline` and all Koopman lifting functions. These functions provide a more convenient way to use a fit Koopman model outside of `scikit-learn` (e.g. in control applications) (https://github.com/decarsg/pykoop/pull/61)
* Added `predict_trajectory()` as a replacement for `predict_multistep()`, which is now deprecated. This new function provides a more convenient interface for use outside of `scikit-learn`, and also supports global Koopman predictions, where states are not retracted and re-lifted between timesteps (https://github.com/decarsg/pykoop/pull/65).
Enhancements
* Overhauled organization of Sphinx docs (https://github.com/decarsg/pykoop/pull/66)
* Updated examples and added binder links to Juypter notebooks (https://github.com/decarsg/pykoop/pull/70, https://github.com/decarsg/pykoop/pull/71).
* Refactored unit tests, and enabled remote testing and doctests in CI (https://github.com/decarsg/pykoop/pull/67).
Bug fixes
* Fixed a serious bug in `predict_multistep()` where only the first episode was scored (https://github.com/decarsg/pykoop/pull/65).
* Allowed force quitting LMI regressor using `^C` twice (https://github.com/decarsg/pykoop/pull/54).
* Stopped doctests from failing due to floating point comparisons (https://github.com/decarsg/pykoop/pull/58).