ENH: maintain GLM wrapper + enhance set of reported statistics with zstat

Added zstat at the end of the 'list' to maintain compatibility

While at it I wonder

- shouldn't this be implemented as Learner where in _train it fits the model and in forward implements contrasts provided in input dataset?

- now it mixes what to do (just fit, contrast) with what to output (params, tvalues, bunch of contrast stats) in a single option (res).  Should it may be separate those apart and allow for multiple contrasts to be output for a single model fit? e.g.

```t={'F>C': [0,1], 'C>F': [1, 0]}, F={'F|C', [[1, 1]]}```

with default (empty) res would produce a dataset with samples containing all gory details (estimates, ttest, contrast stats) with "test" and "descr" containing description of what each sample is?

and then ```res``` could be specified to point which samples to output, or may be we could/should get a generic Mapper which select samples based on specification (pretty much to replace good old PyMVPA 0.4 Dataset.select or whatever it was called)?