Greenwood Roadmap

Greenwood is built in dependency-ordered, individually shippable steps. This is the public capability roadmap. It lists only what hasn’t shipped yet. See the API reference for everything that has.

Planned — Long Term

Advanced estimators for complex survival problems and specialized applications.

Additive Hazards & Cure Models

Refinements to the existing AalenAdditive and MixtureCure models.

  • Constrained estimation in AalenAdditive that guarantees non-negative hazards and proper survival functions (predicted cumulative hazards are currently clipped at zero)
  • 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.

  • 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

R survival Coverage

Features of R’s survival package that Greenwood doesn’t have yet.

  • Expected and relative survival against population rate tables (survexp())
  • Person-years tables (pyears())
  • Building time-varying covariate data from event and covariate tables (tmerge()), beyond what split_episodes() covers
  • Time-transform terms in Cox models (tt())
  • Consistency checks for multi-state and counting-process data (survcheck())

Platform & Interop

Performance and ecosystem integration toward 1.0.

  • Full backend matrix algebra with accelerated kernels (JAX/Numba) for ultra-large datasets (100k+ rows), extending the Numba kernel the survival forests already use
  • 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.