Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 74.8%. Comparing base (374de06) to head (28ab8db).
⚠️ Report is 2 commits behind head on main.

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One open question is on estimation strategy.

While we can use pure python and MLE to recover the parameters, this will likely be quite slow/inefficient. So three strategies get us out of this:

1. We could try to numba/cythonize the pairwise dependence loop,
2. We could do something nonparametric with Graph/W to sparsify the pairwise dependence loop

Alternatively, I think this would be case where moving to a Bayesian estimator might kill two birds with one stone.... I also don't know yet how to do inference on the parameters in MLE using a permutation approach yet. Any permutation-based inference will be seriously expensive, since we have to deal with randomizing over space, time, and mark.

But, if we implement this log likelihood in stan/pymc, we could get estimation (only) using their MAP estimators, or also support inference using sampling.