{"library":"scikit-survival","type":"library","category":null,"description":"Scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides various survival models like Cox proportional hazards, random survival forests, and gradient boosting, along with utility functions for data preparation and evaluation. The current version is 0.27.0, and it follows an active release cadence, frequently updating to support newer versions of scikit-learn, NumPy, and pandas.","language":"python","status":"active","version":"0.27.0","tags":["survival-analysis","scikit-learn-compatible","machine-learning","healthcare","statistics"],"install":[{"cmd":"pip install scikit-survival","imports":["from sksurv.ensemble import RandomSurvivalForest","from sksurv.linear_model import CoxPHSurvivalAnalysis","from sksurv.datasets import load_whas500","from sksurv.metrics import concordance_index_censored"]}],"homepage":null,"github":"https://github.com/sebp/scikit-survival","docs":"https://scikit-survival.readthedocs.io","changelog":"https://scikit-survival.readthedocs.io/en/latest/release_notes.html","pypi":"https://pypi.org/project/scikit-survival/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":40,"avg_install_s":14.4,"avg_import_s":4.25,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/scikit-survival/compatibility"},"provenance":{"verified_status":"passing","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Tue Jul 28","install_tag":null}}