This final week, I've managed to finish the deprecation PR, merge the VAR model implementation and fix some bugs to allow for backwards compatibility. I also discussed future potential improvements that could be made in the package. I've thus been adding additional examples to fully document the capabilities that were added to the package during GSoC. Towards the end of the week, I will make a v0.2 release on pypi.

This week, I've still been now finalized the deprecation PR, but waiting on mentor(s) and other developers to chime in regarding a very weird numpydoc error that comes up in our sphinx build. This to mine and my mentor's eyes doesn't look like it is caused by my PR changes. I have also added comparisons to statsmodels VAR model implementation with order-2 and order-1, thus validating that our implementation is just as good for VAR models.

What Have I Been Working On: This week, I’ve finished the deprecation of the connectivity sub-module in mne-python. It was actually more complicated then I originally thought and each time there was additional CI failures due to the interactions in various tutorials to the connectivity module. I have also now been working on comparing the vector auto regression to the implementation on statsmodels in both accuracy and also runtime performance. What Have I Been Struggling With: Mostly I have been struggling with CI failures that are difficult to debug due to my inexperience. There are various pytest and sphinx complexities that I was not used to, but it's been quite informative learning about various ways to customize pytest and sphinx in order to perform more complex unit testing and documentation building.

This week, I've been wrapping up examples showing the VAR model and incorporating mentor comments. I've also finalized the deprecation of mne.connectivity within the mne-python module.

What Have I Been Working On: This week, I’ve been working on finishing the vector autoregressive model addition, connectivity examples showing off the VAR model and adding a "connectivity for classification" example. This involves working a lot with scikit-learn's API to assist transformation of functions into scikit-learn compatible pipelines. The other examples require me to learn how to best simplify explanations for new readers. My plan for the upcoming week is to finish these functional improvements, taking in feedback from my mentors. I intend on then adding additional tests, and code that deals with bivariate statistical tests, or graph statistical tests. What Have I Been Struggling With: Writing good examples that are useful for package users is difficult, so there's a lot of iteration that I have been working on with my mentors.