Release v1.7

Started "ProgPy Short Course": A series of Jupyter Notebooks designed to help users get started with ProgPy and understand how to use it for prognostics. See https://github.com/nasa/progpy/tree/master/examples
Updates to improve composite model:
- Support setting parameters in composed models using [model].[param] format (e.g., composite_model["model1.Param1"] = 12)
- Support adding functions to composite. Useful for simple translations
Prediction and Simulation event strategy. For models with multiple events can now specify if you would like prediction or simulation to end when "first" or "any" of the events are met
Updates to parameter estimation
- Users can now estimate nested parameters (e.g., parameters['x0']['a']) using a tuple. For example params=(('x0', 'a'), ...)
- MSE updated to include a penalty if model becomes unstable (i.e., returns NaN) before minimum threshold. This encourages parameter estimation to converge on parameters for which the model is stable
Tensorflow no longer installed by default (this is important for users who are space constrained). If you're using the data-driven features install ProgPy like so: pip install progpy[datadriven] or pip install -e '.[datadriven]' (if using local copy)
Support for Python 3.12
Removed some warnings
Various Bugfixes and Performance optimizations

Notes for upgrading:

If you're using the data-driven features install ProgPy like so: pip install progpy[datadriven] or pip install -e '.[datadriven]' (if using local copy)
Use "events" keyword instead of "threshold_keys" in simulation