(WIP) Sparse models: S-LASSO and TV-l1

- Supports TV-l1 and S-LASSO priors
- Supports logistic and squared losses
- Has cross validation
- Can automatically select alpha by CV (+ automatic computation of useful alpha ranges for the CV)
- Warning: User must supply l1_ratio
This might be a good place to briefly render explicit the relation between "L1" (refers mostly to the L1-Norm penalty, AFAIK, independent of a specific optimization problem) and "Lasso" (refers to the regression problem including regularization by L1-Norm, AFAIK). I feel that terms such as "Lasso" and "Ridge" are jargon terms that are difficult to investigate for non-machine-learning folks. More concretely, on could at least mention that "TV-L1 and Smooth-Lasso have the L1 penalty in common but differ in the type of spatial prior." or something like that