At Parietal, we believe that high-quality open-source scientific software is an important aspect of research in computational methods. For these reasons, we invest heavily in community-driven projects, some directly linked to Inria, and others living their own life. The different members of Parietal are committers to a variety of open-source projects and we hire research engineers and programmers dedicated to the projects.
scikit-learn is a Python module for machine learning. It is an open source (BSD-licensed) library that exposes many standard algorithms for supervised and unsupervised classification. It is shared by the python scientific community. Parietal is actively contributing to scikit-learn with the full-time involvement of Jaques Grobler and Olivier Grisel.
NiLearn is a Python module for fast and easy statistical learning on NeuroImaging data with a focus on fMRI data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
MNE-Python is the Python open source toolbox for processing and visualizing MEG and EEG data. It facilitates a wide range of data processing tasks, including artifact rejection, diagnostic visualizations, multivariate pattern analysis, source localization, time-frequency analysis and statistics. MNE-Python is developed by an international community of researchers from diverse laboratories.
It is used internally by other libraries such as scikit-learn, nilearn and MNE-Python.
Parietal team members have also been involved in the development of: