Support for regression stream and deconfouding in nested CV

• Major feature: Ability to predict continuous variables (regression)
• Major feature: Ability to handle confounds (regress them out, augmenting etc)
• Redesigned the internal structure for easier extensibility
• New CVResults class for easier management of a wealth of outputs generated in the Classification and Regression workflows
• API access is refreshed and easier

Support for Missing data

• Good news: neuropredict can handle missing data now (that are encoded with numpy.NaN). This is done respecting the cross-validation splits without any data leakage.
• Few other useful utilities

More options for your diverse problems!

• new classifier : SVM
• new flag to choose a feature selection method
• user chosen options now saved to disk, to better handle complex interactions between options
• code clean up and faster tests

Much faster with more classifiers!

• Parallelizing the main the CV loop, leading to great reduction in total time for report generation!
• More options, including choice of different classifiers (Random Forest and Extra Trees classifiers)
• Support for dataset in Weka's ARFF format
• Better visualizations (handling small/nan values in feature importance, layouts and design)
• auto versioning!
• Ability to read meta data from pyradigms or ARFF files, without having to specify that separately.
• Dropping support for Python 2.7 :(

Release to obtain a DOI for citation purposes

v0.3-DOI

matching n_jobs with predispatch in gridsearch!