[ENH] Group-sparse precision estimation, by Honorio & Samaras.

This is an implementation of the algorithm described in 

*Jean Honorio and Dimitris Samaras "Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models", arXiv:1207.4255 (17 July 2012), http://arxiv.org/abs/1207.4255.*

Given several sets of input signals, it computes several sparse precision matrices at once, using a common sparsity pattern.

The implementation itself is already fairly stable and optimized. It remains mainly some technical issues (like API, argument names, etc.). Namely:

- [x] Settle on names for functions and classes, the current ones may not be the best (group_sparse_covariance GroupSparseCovariance and GroupSparseCovarianceCV)
- [x] Clean up handling of stopping criteria (esp. the _group_sparse_covariance_costs function)
- [x] Separate in GroupSparseCovarianceCV the parameters for selection of alpha and parameters for final fit.
- [x] Automatically normalize input signals.
- [x] Clean up user messages, use the logging system of PR #84 (or similar). 
- [x] Write some documentation on the inner working of the CV version, namely the modified stopping criterion to stop optimization.
- [x] Rename the internal variable `Winv` into `W_inv`
- [x] Rename the regularization parameter `rho` to `alpha`, to match scikit-learn's convention. This implies renaming an internal variable already called `alpha`
- [x] add support for caching in GroupSparseCovariance|CV