scikit-survival-analysis
byjaechang-hits· 57 skills
Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.
How to install scikit-survival-analysis?
jaechang-hits/sciagent-skills/scikit-survival-analysis
$npx -y skills add jaechang-hits/sciagent-skills --skill scikit-survival-analysisInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/jaechang-hits/sciagent-skills" --skill "jaechang-hits/sciagent-skills/scikit-survival-analysis"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/jaechang-hits/sciagent-skills" that are relevant to the current task. Run `npx skills add "https://github.com/jaechang-hits/sciagent-skills"` and select the relevant skills, then follow their instructions.
Related
6 picksType
- azure-kustoQuery and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis.Jul 2026
- analyticsWhen the user wants to set up, improve, or audit analytics tracking and measurement.Jul 2026
- firecrawl-dashboard-reportingPull metrics from analytics dashboards and internal web tools with Firecrawl browser.Jun 2026
- data-storytellingTransform data into compelling narratives using visualization, context, and persuasive structure.Jul 2026
- data-visualizationCreate effective data visualizations with Python (matplotlib, seaborn, plotly).Jul 2026
- extension-oqlMake a canister's data queryable by the Caffeine Data Intelligence agent.Jul 2026
