Need to do some better handling of low-observation models in plot_diagnostics. These are models that shouldn't really be estimated, and we can't really make the plots work, but we shouldn't raise exceptions.

• Any dataset with less than 10 observations will raise an error computing the error autocorrelations:

mod = sm.tsa.statespace.SARIMAX(np.random.normal(size=10), order=(10, 

Hey Contributor!

Thanks for checking out HyperLearn!! Super appreciate it.

Since the package is new (only started like August 27th)..., Issues are the best place to start helping out, and or check out the Projects tab. There's a whole list of stuff I envisioned to complete.

Also, if you have a NEW idea: please post an issue and label it new enhancement.

In terms of priorities, I wanted

We need to add a yes and no label on the arrows when we plot a tree interpreter. Also we should be ideally using the population summary inference values, when calculating p-values and CIs at the leafs.

Need more robust documentation of data sets "benefits", "beveridge", "happiness", and "hprice3". These were missing from my copy of Wooldridges` Data Set Handbook.

So far we have relied largely on demonstrative notebooks
at https://github.com/rsvp/fecon235

The source code here at fecon236 is designed to be more
explicit than casual usage, so if you have experience in
setting up a nice documentation framework, please
kindly volunteer.

The current "docs" need a complete redo, one that starts from fresh eyes.
Nobody peeks at the source code unless t

@keblu we should update the way people cite the package; check my 3 points added to the github page. I follow David Hsieh practice. We should now push so that people cite our contribution. Please fix the webpage in line with Github. Thx