No solution when using engine="numba" for Kalman Filter

[martinvonk]

For some datasets, the optimization does not succeed due to a ZeroDivisionError in SciPy.

Can be reproduced with the following dataset: test.csv

df = pandas.read_csv(test.csv, index_col=0, parse_dates=True)
mt = metran.Metran(df)
mt.solve()

The issue can be resolved by using in the SPKalmanFilter(engine="numpy"). I discussed this issue with @dbrakenhoff and we suspect that engine="numpy" is more robust since it fills in inf or nan for logarithms and fractions automatically while engine="numba" does not.

This can be resolved by allowing the user to specify the SPKalmanFilter engine which is currently only possible by changing the source code.

[dbrakenhoff]

Option for passing engine has been implemented by #13.

Issue remains open to figure out why engine="numba" fails and engine="numpy" produces a communality >100% and some runtime warnings about illegal values in np.log.

Perhaps @wlberendrecht can take a look sometime 😇 ?