{"library":"nilearn","type":"library","category":null,"description":"Nilearn is a Python library for statistical learning with neuroimaging data. It provides tools for general linear model (GLM) based analysis and leverages the scikit-learn toolbox for multivariate statistics, including predictive modeling, classification, decoding, and connectivity analysis. The current stable version is 0.13.1, and releases occur regularly, often including new features, enhancements, and deprecations.","language":"python","status":"active","version":"0.13.1","tags":["neuroimaging","fMRI","machine-learning","brain-imaging","scientific-computing","data-analysis"],"install":[{"cmd":"pip install -U nilearn","imports":["from nilearn import datasets","from nilearn import plotting","from nilearn.maskers import NiftiMasker","from nilearn import image","from nilearn.decoding import Decoder"]},{"cmd":"conda create -n nilearn_env python=3.10\nconda activate nilearn_env\npip install -U nilearn","imports":[]}],"homepage":"https://nilearn.github.io","github":"https://github.com/nilearn/nilearn","docs":null,"changelog":"https://nilearn.github.io/stable/changes/whats_new.html","pypi":"https://pypi.org/project/nilearn/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":15.9,"avg_import_s":5.49,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/nilearn/compatibility"},"provenance":{"verified_status":"install_fail","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Sun Jul 05","install_tag":null}}