Sandwich mle

next round of sandwich robust covariance matrices

sandwiches for MLE, mainly discrete models 
similar to PR #1189 which added it to Regression models

issues:
#863 for cluster robust based on score
#1158 main overview issue with link to other issues

Changes
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- generalize sandwich_covariance.py to take score, hessian_inv  DONE
   - added unit test for Poisson and NegativeBinomial for cluster so far
   - I made it backwards compatible, it still accepts results instance, but requires tuple for (jac, hessian_inv)
- no use of `wresid` for WLS, GLS yet
- add wrapper function that dispatches to individual sandwiches, like the list in `RegressionResults.get_robustcov_results`, to `sandwich_covariance.py`. intended as single point of entry for all sandwiches
- integrate with discrete results classes
- make additional results use robust statistics, e.g .wald_null instead of LL-Null in summary see #1104

Options, Design Problems
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- degrees of freedom and small sample corrections: Stata uses a different correction for cluster robust after Count regression models than after linear regression models.  (small number of group adjustment but no nobs-k_vars adjustment)
- not checked yet: Which HCx is Stata's robust after Poisson. 
- should we rename jac to score_obs?
- add `cov_type` option consistently to fit across all models where it applies.
- still to figure out: what's the easiest way to get the sandwiches for GLM? need the score equivalent, or WLS results (?)
- also open non- or limited heteroscedasticity robust cov: AC, SUR structure, variations on (X'uu'X = Sigma * X'X)
  (only example so far is replicating OLS in GMM). 
  These cases cannot use score directly, but need X, u in X*u separately, AFAICS.
- demean score?  I just saw this in PR #1214 and don't remember what I read (demeaning by cluster?). 
  (Bruce Hansen recommends demeaning for GMM which is the default that I use. Stata uses no demeaning as default in GMM.)