Association-measures

- new AM: conservative log ratio with correct CI boundary from Poisson distribution (Evert 2022)
- include `wheel` as build dependency in `pyproject.toml`
- use `score()` rather than `calculate_measures()` in tests; extend propagation of `score()` parameters
- include `pytest.ini`
- simplify `setup.py`

update requirements
- maximum of version numbers s.t. python3.6
- specify wheel as requirement (for building)

two new measures:
- minimum sensitivity
- Liddell

- new possible input: "keyword-friendly" corpus frequencies notation (f1, N1, f2, N2)
- new `score` wrapper also allows constant integer counts (N1, N2 for keyword notation; f1, N for frequency signatures) to be given as parameters
- keyword arguments are now passed from `calculate_measures()` (and `score()`) to underlying measures

compatibility:
- require scipy instead of python3.8

measures:
- local MI
- simple LL
- extend parameters of conservative log ratio (Sidak correction)

sort & categorize measures:
- asymptotic hypothesis tests
- point estimates of association strength
- (likelihood measures)
- information theory
- conservative estimates

universal discounting for zeros:
- O11 and O12 are set to a small value where they're 0 (0.001 by default; except for Hardie's dubious discounting of 0.5)
- this makes additional definition of phi-function obsolete
- NB discounting does not have any effect for some measures (log-likelihood, local MI)

What's Changed
* github workflows by ausgerechnet in https://github.com/fau-klue/pandas-association-measures/pull/20
* V0.1.6 by ausgerechnet in https://github.com/fau-klue/pandas-association-measures/pull/19


**Full Changelog**: https://github.com/fau-klue/pandas-association-measures/compare/v0.1.5...v0.1.6