Greenwood Roadmap

Greenwood is built in dependency-ordered, individually shippable steps. This is the public capability roadmap.

Planned — Near Term

Descriptive and exploratory features building on the core estimators.

Model Validation and Performance

Robust cross-validation and performance assessment for imbalanced survival data.

  • Performance optimization for large datasets (memory efficiency, computation speed)
  • Multi-metric cross_validate: accept metrics as a list so concordance, Brier score, and time-dependent AUC can all be evaluated in a single CV run and returned as a keyed dict

Survival Predictions

Point-in-time and distributional predictions from fitted models.

  • predict_median: median survival time (time at which the survival curve crosses 0.5) for regression models (CoxPH, AFT, RoystonParmar) with confidence intervals
  • predict_quantile: generalized quantile survival time at any probability level
  • predict_expectation: expected survival time using RMST as the tail assumption, with an explicit tau (truncation time) parameter to make the tail assumption transparent

Confidence Intervals & Inference

Systematic confidence interval and standard error support across all estimators.

  • Bootstrap and analytical methods for uncertainty quantification
  • Predictive intervals for time-varying Cox model forecasts
  • Robust (sandwich) variance for Kaplan-Meier: correct CI coverage for inverse-probability-weighted curves, replacing Greenwood’s formula with a sandwich estimator when non-integer weights are present (analogous to robust=True in R’s survfit)
  • Clustered robust variance for Kaplan-Meier: extend the sandwich estimator to handle correlated observations (e.g., recurring events, patients nested in clusters), matching R’s cluster() term in survfit formulas

Planned — Medium Term

Regression model extensions and flexible semi-parametric approaches.

Cox Residual Diagnostics

Outlier detection and case-level assessment for Cox models.

  • Leverage and hat-matrix diagnostics
  • Visualizations for outlier and influential point detection

Advanced Proportional-Hazards Tests

Extended testing of the Cox model assumptions.

  • cox_zph with Kaplan-Meier and rank-based time transforms
  • Time-stratified tests for non-proportional hazards
  • Smooth non-linear hazard ratio curves (smoothHR-style): spline-based visualization of covariate effects on the log-hazard scale, complementing cox_zph() for diagnosing non-linearity in continuous predictors

Flexible Parametric Models

Semi-parametric and parametric spline-based hazard regression.

  • Piecewise exponential models with optimal knot selection
  • Generalized gamma regression (encompasses Weibull, log-normal, exponential)
  • AIC and BIC for penalized Cox models (CoxNet): useful for model selection on small datasets and regulatory submissions where cross-validation is impractical

Planned — Long Term

Advanced estimators for complex survival problems and specialized applications.

Additive Hazards & Cure Models

Alternative hazard structures and zero-inflated survival models.

  • Aalen additive model for additive (vs. proportional) hazard regression with constrained optimization to ensure non-negative hazards and proper survival functions
  • Mixture cure models for populations with long-term survivors
  • Non-parametric maximum likelihood estimation (NPMLE) for cure fractions
  • Goodness-of-fit tests and model comparison for cure models

Advanced Competing Risks & Multi-State

Extended methods for cause-specific and multi-state analyses.

  • Gray’s test for differences in cumulative incidence across groups
  • Variance estimation for multi-state transition probabilities
  • Pseudo-observation approach for CIF and multi-state occupancy regression
  • Custom estimands via pseudo-observations framework

Frailty Models

Random-effects Cox models for correlated survival data.

  • Conditional and marginal predictions for known and new clusters

Advanced Performance Metrics

Discrimination and calibration assessment beyond point-in-time.

  • Integrated discrimination improvement (IDI) and net reclassification improvement (NRI)
  • Time-dependent Brier score refinements and sensitivity analyses
  • concordance_index_ipcw: inverse-probability-of-censoring-weighted concordance index for time-dependent discrimination (more robust than Harrell’s C under heavy censoring)
  • ipc_weights / CensoringDistributionEstimator: IPC weights as first-class utilities, enabling manual reweighting for metrics and models beyond concordance

Machine Learning Survival Estimators

Tree-based and ensemble survival models that output individual survival functions rather than scalar risk scores.

  • SurvivalTree: single survival tree using the log-rank split criterion, which is the foundation for ensemble methods and useful for interpretable non-parametric survival modeling
  • RandomSurvivalForest: ensemble of survival trees with bootstrap aggregation (the most widely used ML survival model, returns per-subject survival and cumulative hazard functions)
  • ExtraSurvivalTrees: extremely randomized variant of RSF with additional split randomization for faster training and lower variance on large datasets
  • GradientBoostingSurvivalAnalysis: component-wise and tree-based boosting for survival (strong predictive performance on structured tabular data)
  • IPCRidge: IPC-weighted ridge regression (a linear survival model that corrects for censoring bias via inverse-probability reweighting rather than the partial likelihood)

Platform & Interop

Performance and ecosystem integration toward 1.0.

  • Full backend matrix algebra with accelerated kernels (JAX/Numba) for ultra-large datasets (100k+ rows)
  • Finalized extension protocols and Narwhals dataframe backend completeness
  • Full interoperability with Great Summaries (tbl_survfit, tbl_regression)
  • Migration guides for users transitioning from R’s survival package

Feedback & Contributions

Have ideas for features not listed here? Open an issue with the enhancement label! Contributions to any planned item are welcome so check existing issues first to avoid duplication.

This roadmap is a living document. It is updated as features are added and new priorities emerge.