(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
> Maybe I'm wrong, but that basically excludes all ROI based analyses from
> benefitting from sensible out-of-the-box settings, right? (I am not mentioning
> single subject analyses as an argument, but one may want to accommodate those
> as well in the future.)

Quite on the contrary. With the settings being a percentile of the voxels
(which is basically what you are advocating), when doing ROI-based
analysis, the default give meaningless selections. For instance selecting
40 voxels. This is exactly what happened earlier, and what prompted me to
ask for this specific implementation.

> As @bthirion says, a volume percentile of a specified mask seems as
> sensible to me as selecting a percentile can get in the context of this
> algorithm.

Honestly, please no. Think about it. If people specify a small mask, as
in the Haxby dataset, and leave the default settings (which 90% of the
users will), they will get nonsense. And it's not like it's nonsense that
is easy to diagnose.