Robust covariances: integrate with models, normal based Wald tests

first round of integrating robust covariances into the models, and make the cov_type handling more consistent across models.
plus `add_t` normal distribution based Wald statistics

changes behavior of RLMResults, see #1164

currently after OLS only, still needs to be extended to other models. (might currently be wrongly inherited by WLS/GLS)
OLS robust covariance integration tested against Stata, especially ivreg2

WIP: there is a lot more to do, but should be merged soonish, so it can be merged with @bashtage 's PRs

main discussion and list of issues is in #1158

(need TravisCI to check because I'm not always running the full test suite, made mistake in RLM)

for **changelog**

- add normal and chisquare based wald test to all models.
  `t_test` and the new `wald_test` in LikelihoodModelResults now can now also use normal resp. chisquare distribution. 
  The results instances can use a `use_t` boolean attribute to indicate whether the t or the normal distribution is used in `t_test` and whether F or chisquare distribution is used in `wald_test`. `use_t` also determines the distribution in `pvalues` and `conf_int` of a model.
 The default `use_t` corresponds to the previous use of the t or normal distribution in `pvalues`, all models except for the linear models, OLS, WLS and GLS, use the normal distribution by default.
TODO: some models (RLM, ...) still use hardcoded normal distribution
- improved ContrastResults after t_test, which now also create a parameter summary table
- integrate robust covariance matrices with RegressionResults. `get_robustcov_results` was added as a method to RegressionResults that creates and returns a new result instance that uses the requested robust covariance for all inferential statistics and tests, pvalues, conf_int, f_test, t_test.

refactoring:
- RegressionResults: covHCx and HCx_se are now cached attributes
- robust.RLMResults now sets the requested covariance matrix of the parameters as default and is used for all inferential statistics and tests. Before bse, tvalues and pvalues were already based on the robust covariance matrix of the parameter estimates `bcov_scaled`.