Skills

A skill is a package of structured files that teaches an AI coding agent how to work with a specific tool or framework. The skill below was generated by Great Docs from this project’s documentation. Install it in your agent and it will be able to run commands, edit configuration, write content, and troubleshoot problems without step-by-step guidance from you.

Any agent — install with npx:

npx skills add https://rich-iannone.github.io/greenwood/

Codex / OpenCode

Tell the agent:
Fetch the skill file at https://rich-iannone.github.io/greenwood/skill.md and follow the instructions.

Manual — download the skill file:

curl -O https://rich-iannone.github.io/greenwood/skill.md

Or browse the SKILL.md file.

SKILL.md

---
name: greenwood
description: >
  Modern survival analysis for Python: Narwhals-native, R-validated, beautifully visualized. Use when writing Python code that uses the greenwood package.
license: MIT
compatibility: Requires Python >=3.10.
---

# greenwood

Modern survival analysis for Python: Narwhals-native, R-validated, beautifully visualized.

## Installation

```bash
pip install greenwood
```

## API overview

### The response

The Surv object, the spine of every analysis.

- `Surv`: A validated time-to-event response for survival analysis
- `CensoringType`: The censoring flavor of a `Surv` response

### Non-parametric estimators

Kaplan-Meier survival and Nelson-Aalen cumulative hazard.

- `KaplanMeier`: Kaplan-Meier product-limit estimator of the survival function
- `NelsonAalen`: Nelson-Aalen estimator of the cumulative hazard

### Regression

Cox proportional hazards and parametric AFT models.

- `CoxPH`: Cox proportional hazards model
- `CoxNet`: Elastic-net penalized Cox proportional hazards model
- `ZPHResult`: Proportional-hazards test results (Grambsch-Therneau)
- `AFT`: Parametric accelerated failure time model
- `RoystonParmar`: Royston-Parmar flexible parametric survival model (proportional hazards scale)

### Competing risks & multi-state

Cumulative incidence, the Fine-Gray model, and multi-state transition probabilities.

- `AalenJohansen`: Aalen-Johansen estimator of cumulative incidence functions for competing risks
- `FineGray`: Fine-Gray subdistribution hazard model for a competing-risks endpoint
- `MultiState`: Aalen-Johansen estimator of multi-state transition and occupancy probabilities

### Group comparisons

The log-rank test, trend tests for ordered groups, the G-rho (Fleming-Harrington) family, and restricted mean survival time (RMST) comparisons.

- `logrank_test`: Compare survival across groups using the weighted log-rank (G-rho) test
- `trend_test`: Test for linear trend across ordered groups using the log-rank test family
- `pairwise_logrank_test`: Pairwise log-rank tests for all group pairs with multiple-comparison correction
- `TestResult`: The outcome of a log-rank group comparison test
- `rmst_test`: Test for equality of RMST across two or more groups
- `rmst_diff`: Compute RMST difference between two groups with confidence interval
- `pairwise_rmst_test`: Pairwise RMST tests for all group pairs with multiple-comparison correction
- `RMSTResult`: Results of an RMST comparison test or difference calculation
- `logrank_n_events`: Number of events needed for the log-rank test to reach a target power
- `logrank_power`: Power of the log-rank test given the number of observed events
- `logrank_sample_size`: Total sample size needed for the log-rank test to reach a target power

### Prediction performance

Concordance and the IPCW Brier score.

- `concordance_index`: Harrell's concordance index: discrimination of risk scores against observed survival
- `brier_score`: IPCW (Graf) Brier score of predicted survival probabilities at specified times
- `integrated_brier_score`: Integrated (time-averaged) Brier score across multiple time points
- `cross_validate`: Evaluate a survival model's out-of-sample performance using k-fold cross-validation

### Visualization

plotnine survival curves and aligned numbers-at-risk tables.

- `plot_survival`: Plot Kaplan-Meier survival curve(s) with Altair
- `risk_table`: Return the numbers-at-risk table as a standalone Altair chart

### Core kernel

The risk-set / event-table tabulation shared by KM, log-rank, and Cox.

- `EventTable`: Per-time risk-set tabulation (optionally within strata)
- `event_table`: Tabulate the event history: risk sets and events at each observed time

## Resources

- [Full documentation](https://rich-iannone.github.io/greenwood/)
- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs
- [Source code](https://github.com/rich-iannone/greenwood)