Overall Objectives
Research Program
Application Domains
Software and Platforms
New Results
Partnerships and Cooperations
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Section: Software and Platforms


Participants : Bertrand Thirion, Gaël Varoquaux [correspondant] , Philippe Gervais, Jaques Grobler, Alexandre Gramfort, Fabian Pedregosa, Alexandre Abraham, Michael Eickenberg.

NiLearn is the neuroimaging library that adapts the concepts and tools of the scikit learn to neuroimaging problems. As a pure Python library, it depends on scikit learn and nibabel, the main Python library for neuroimaging I/O. It is an open-source project, available under BSD license. The two key components of NiLearn are i) the analysis of functional connectivity (spatial decompositions and covariance learning) and ii) the most common tools for multivariate pattern analysis. A great deal of efforts has been put on the efficiency of the procedures both in terms of memory cost and computation time. NiLearn is maintained both through the help of Inria: (a developer funded by Saclay CRI in 2012-2013, a 2013-2014 ADT, and through the NiConnect project (P. Gervais).