Discrete cleanup

This closes #225 and #515. There are now correct residuals defined for count models and multinomial logit and additional residuals available for binary models

In the case of count models this is just residuals are just y - cdf(XB).

In the case of multinomial models it's a simple hit or miss. 0 is correct prediction. 1 is a miss. This is what gretl does.

In the case of the binary models, I deprecated the resids attribute in favor of using resid_dev for deviance resids. I also added Pearson residuals, and "generalized residuals" defined in 

    Gourieroux, C., A. Monfort, E. Renault and A. Trognon (1987) ‘Generalized residuals’, Journal of Econometrics 34: 5–32.

These are what gretl uses.

All of these are now tested.