Py-nilearn

Jul 20, 2023

Statistical learning for neuroimaging in Python

Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation & open community.

It supports general linear model GLM based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Nilearn now includes the functionality of Nistats. Here’s a guide to replacing Nistats imports to work in Nilearn.



Checkout these related ports:
  • Zx - MQT ZX A library for working with ZX-diagrams
  • Zotero - Reference management for bibliographic data and research materials
  • Yoda - Particle physics package with classes for data analysis, histogramming
  • Xtb - Semiempirical Extended Tight-Binding Program Package
  • Xmakemol - Molecule Viewer Program Based on Motif Widget
  • Xdrawchem - Two-dimensional molecule drawing program
  • Xcrysden - Crystalline and molecular structure visualisation program
  • Xcfun - Exchange-correlation functionals with arbitrary-order derivatives
  • Wxmacmolplt - Graphical user interface principally for the GAMESS program
  • Wwplot - Plotting tool for experimental physics classes
  • Wannier90 - Maximally-localized Wannier functions (MLWFs) and Wannier90
  • Votca - CSG and XTP libraries for atomistic simulations
  • Voro++ - Three-dimensional computations of the Voronoi tessellation
  • Vmd - Molecular visualization program
  • Vipster - Crystalline and molecular structure visualisation program