Release 0.12.0 (beta)

This is the first beta release of the load_confound project. Updates include:

• code refactoring. The code (and the tests) are more modular, and each module is kept short. This refactoring impacted the compcor method in particular.
• support for gifti surface data files.
• checks and sensible error messages to match input files with specific strategies. Note that it is not possible anymore to directly specify a tsv confound file, as the target imaging file is necessary to assess that a particular strategy is sensible.
• better documentation for ICA AROMA.
• full redesign of the denoising strategies. Instead of following a particular benchmark, the denoising strategies are now based on the major technical variants used in the literature, and are quite customizable.

Congratulations to all contributors for reaching the first beta release, and special thanks to @htwangtw for all the hard work that went specifically into that new release 🎉 🎉 🎉

The next release will improve support for 'hard' scrubbing, and will likely introduce a new API. The load_confounds project is also set to merge inside nilearn and will be discontinued as a stand-alone repository when the merge is complete.