Mogp-emulator

Minor update to update the repository name to put mogp on PyPI. Also adds a demo from the 25 September 2020 Excalibur workshop.

Changes in this release:

* Add `excalibur_workshop_demo.py` to the `demos` directory
* Change URLs to git repo
* `mogp-emulator` can now be installed via PyPI

This is a micro bugfix release to fix an issue that occasionally came up when fitting emulators.

Changes in this release:

* Corrected a bug in the kernel distance metric computation and added tests to catch the situation.

This is a major update, incorporating a complete rewrite and upgrade of the GP class and many of the other core library components. It is not backwards compatible with previous versions of the library and it has removed some pieces of the GP class, but it should improve flexibility going forward. A number of documentation additions have also been made to improve presentation and help users understand what is going on.

Specific improvements that have been made:

* Refactor of the GP class to externalize fitting. A GaussianProcess object now more closely represents the mathematical definition in that routines for estimation/fitting have been externalized. MCMC sampling for estimation has been removed for the moment in this process, though I anticipate being able to add it back in the future.
* Addition of mean functions. Mean functions can be added using a string formula in a similar manner to R. This optionally uses the patsy library for creating models (and in the future, design matrices), though patsy is not required to use the base mean function implementation.
* Basic support for prior distributions on hyperparameters has been added. This should be improved in future releases.
* Predictions can now include/exclude the nugget from the variance as desired.
* Documentation improvements and examples have been added.
* Numerous other bugfixes.

Update master to v0.2.0. New major features in this release:

* Dimension reduction via the DimensionReduction class
* Fitting Emulators with MCMC sampling
* History matching via the HistoryMatching class
* Documentation now includes the MUCM toolkit

There are also several other minor changes, fixes, and improvements in this update, including improved prediction stability and addition of simple demo scripts.

This is a bug fix release to correct the derivative computations in the `predict` method of `GaussianProcess`:

New methods in the generic `Kernel` class:

* `calc_drdx` computes the derivative of the distance metric with respect to the inputs
* `kernel_inputderiv` computes the derivative of the kernel with respect to the inputs
* Correction to the `predict` method to use the generic kernel method
* Unit tests for the new functions

Update to master branch to add new features and begin explicit semantic versioning. Includes the following new features:

* MICE code for sequential design
* More flexible Kernel implementation, including the Squared Exponential and Matern 5/2 kernels
* Automatic documentation build with several versions available
* Codecov reports automatically uploaded
* Explicit version numbers