{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/10"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"Nilearn provides statistical and machine-learning tools for analyzing brain imaging data, supporting GLM-based analysis and multivariate statistics via scikit-learn integration.","skillfed_tags":["neuroimaging","brain-analysis","scientific-computing"],"use_cases":["Perform GLM-based statistical analysis on fMRI data to identify brain regions associated with experimental conditions","Build predictive models for brain decoding tasks using multivariate pattern analysis with scikit-learn integration","Analyze functional connectivity between brain regions and classify brain states from imaging data","Visualize and explore brain volume and surface data with integrated plotting tools","Conduct group-level statistical inference on neuroimaging datasets across multiple subjects"],"what_it_does":"Nilearn is a Python library for statistical and machine-learning analysis of brain imaging data. It integrates scikit-learn's multivariate statistics with neuroimaging-specific tools, supporting general linear model (GLM) analysis on brain volumes and surfaces. The package is designed for researchers and developers working with fMRI, structural imaging, or other neuroimaging modalities who need accessible statistical and predictive modeling capabilities.\n\nThe library depends on core scientific Python packages (numpy, scipy, pandas, scikit-learn, nibabel) for numerical computation and neuroimaging file handling. It includes optional plotting support via matplotlib and plotly. The project maintains active development with regular releases, comprehensive documentation, and a community-oriented approach including weekly drop-in hours for user support.","worth_installing":"Yes. Nilearn is actively maintained, has low install friction, carries a permissive license, and addresses a specific scientific need with established community support. It is well-suited for researchers and developers doing neuroimaging analysis in Python. No known security vulnerabilities."},"id":"nilearn","links":{"html":"https://skillfed.io/packages/nilearn","md":"https://skillfed.io/packages/nilearn.md","pypi":"https://pypi.org/project/nilearn/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"nilearn","python_support":"supports_current","summary":"Statistical learning for neuroimaging in Python"},"popularity":{"monthly_downloads":287281,"position":8035,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.14.0"}