Feature Maturity
Not every feature in ferx-core is equally battle-tested. Some — like FOCE/FOCEI estimation and the analytical PK solutions — have been validated against gold standard engines across many datasets. Others are newer and have only been exercised on a handful of examples. To make this explicit, every major feature carries a maturity label.
Where a feature has its own dedicated reference page, that page repeats its label in a banner at the top, e.g.:
Maturity: beta — see Feature Maturity for what this means.
The table below is the authoritative list; a few features documented inside a shared reference page (e.g. the gradient method under Fit Options) or only in an example carry their label here rather than in a per-page banner.
Maturity levels
| Label | Meaning |
|---|---|
| stable | Well-tested functionality with proven stability across diverse datasets and estimation options. Performance is comparable to (or better than) gold standard NLME engines (NONMEM, Monolix). Safe for production use. |
| beta | Stable in limited testing and across a range of fit settings, but some caution is warranted before relying on it in production on unseen datasets. Validate against a reference where you can. |
| experimental | New functionality tested only on a single example or a small handful of (often toy) examples. Behaviour and syntax may change. Results should be treated as provisional and validated carefully. |
These labels describe ferx-core’s current state and will move upward as features accumulate testing and cross-validation. Most of ferx-core is currently beta, with a mature core approaching stable; a few features remain experimental.
Runtime warnings
Experimental features emit a warning at fit time (surfaced in FitResult.warnings, the CLI output, and ferx check) so their status is visible at the point of use:
| Feature | Warning code |
|---|---|
Stochastic differential equations ([diffusion]) |
W_EXPERIMENTAL_SDE |
Neural networks ([covariate_nn]) |
W_EXPERIMENTAL_NN |
Beta and stable features do not emit a maturity warning.
Feature reference
Model file features
| Feature | Maturity | Reference |
|---|---|---|
| Parameters (theta / omega / sigma, block omega) | stable | Parameters |
| Inter-occasion variability (IOV) | stable | IOV |
| Covariates | stable | Covariates |
| Structural model — analytical PK (1/2/3-cpt) | stable | Structural Model |
| Lag time | stable | Lagtime |
| Steady-state doses (SS) | stable | Steady-State Doses |
| Multiple dosing (ADDL / II) | stable | Multiple Dosing |
| Error model (additive / proportional / combined) | stable | Error Model |
| Simulation | stable | Simulation |
| Individual parameters DSL | beta | Individual Parameters |
| BLOQ / censored observations (M3) | beta | BLOQ |
| ODE models (Dormand-Prince RK45) | beta | ODE Models |
| Built-in absorption models (transit / inverse-Gaussian / Weibull) | beta | Absorption |
| Scaling | beta | Scaling |
| Data selection | beta | Data Selection |
| Derived columns | beta | Derived Columns |
| Output columns | beta | Output Columns |
| Time-to-event endpoints (TTE) | beta | Time-to-Event Endpoints |
| Stochastic differential equations (SDE) | experimental | SDE |
| Neural networks (DCM / NODE) | experimental | Neural Networks |
Adaptive (feedback) dosing — [adaptive_dosing] block / simulate_adaptive() |
beta | Adaptive Dosing |
Estimation features
| Feature | Maturity | Reference |
|---|---|---|
| FOCE / FOCEI | stable | FOCE / FOCEI |
Gauss-Newton (BHHH) — gn, gn_hybrid |
beta | Gauss-Newton |
| SAEM | beta | SAEM |
| SIR | beta | SIR |
| Importance sampling (IMP) | beta | Importance Sampling |
| Outer optimizers (BOBYQA, SLSQP, L-BFGS, MMA, trust-region) | beta | Outer Optimizers |
| Time-to-event estimation (TTE) | beta | Time-to-Event |
The label reflects the engine’s overall confidence in a feature, not the presence or absence of tests — a beta feature may still have extensive automated tests; “stable” additionally requires broad cross-validation against reference engines across diverse datasets.