NF: Randomized Parcellation Based Inference

Tadaaaaam... Implementation of Randomized Parcellation Based Inference [1].

Main accomplished tasks:
* Refactoring: move some functions that were common to permuted OLS and RPBI into a utils.py file.
  orthonormalize_design/matrix, f_score computation, ...
  Tests have been changed accordingly.
* Re-introduce GrowableSparseArrays (and corresponding tests).
  They have already been reviewed in a former PR about permuted OLS before they were finally removed.
  Now, we needed to re-introduce them because we need to address the data efficiently, in different 
  manners and for different purposes.
* Introduce some functions to generate/deal with randomized parcellations.
  They could be moved to a more generic module that serves this purpose (creating multiple
  random parcellations, transforming a dataset with all of the built parcellations)
* Examples: the three examples on massively univariate analysis have been duplicated and RPBI is now
  applied. This is a lot of examples, but they will be structured at some point (see issue #195).
  Issue: examples can take up to 30 minutes on a laptop, using one single CPU (see discussion below).

The code for RPBI is optimized but a run of the algorithm remains costly. This is because RPBI requires the computation of many parcellations (97% of the total execution time). Only 3% of the computation time is spent on the permutations, when 10,000 are performed (which is already kind of a lot, but recommended).
Fortunately, both the computation of the parcellation and the statistical inference part can be parallelized.
Therefore, on a 10-core station, the computation time boils down to less than three minutes, which is acceptable. Still, the examples policy requires that n_jobs=1 by default.

[1] Benoit Da Mota et al. Randomized parcellation based inference, NeuroImage, Volume 89, 1 April 2014.
It is an heuristic formula. The square-root is convenient because it maps `max_elts` between 0 and 1 according to the `sparsity_threshold`. In the case above, we store F-values, which we do not expect to have a uniform distribution, so we expect the number of stored values to decrease as the sparsity_threshold value decrease. The important thing is that the relationship between both is not linear at all so there is a huge gain in using the sqrt.