Capability catalog

This page is the human-readable view of the immutable pymixef.capabilities.CAPABILITIES registry. It contains every registry entry in this source tree: 56 unique capabilities, of which 42 are implemented at their recorded scope and 14 remain evidence-gated. All entries currently carry the deliberately conservative experimental maturity label.

Use the site search with a capability identifier, method name, evidence-file path, or open-gate phrase. Each capability has its own anchored heading, and its registry record expands to show exact state, evidence, and interpretation boundaries.

Start with a usable workflow

The registry is an evidence ledger, not the front door to an analysis. Choose the workflow first, then use the identifier to inspect its exact boundary.

Workflow	Implemented capability set	Main entry points
Gaussian hierarchical models	Dense ML/REML LMM, covariance construction and pathology reporting	LMM-001, COV-001, COV-002; pymixef.fit()
Grouped binary and count outcomes	Laplace Bernoulli, binomial, Poisson, and NB2 GLMM plus separation indicator	GLMM-001, GLMM-003, DIST-001–DIST-003; GLMM guide
Clinical repeated measures	Dense MMRM, covariance checks, and audited pattern-mixture delta adjustments	MMRM-001–MMRM-003; pymixef.data.pattern_mixture_adjust()
Pharmacometric simulation	Canonical events, deterministic ordering, reference ODEs, dose/event integration	DATA-001, DATA-002, ODE-001, ODE-002; Events and ODEs
Population-model building blocks	Conditional objectives/modes, transforms, BQL likelihoods, IOV representation	NLME-001–NLME-004; Population estimation
Uncertainty and robustness checks	Restartable bootstrap, cross-setting refits, whole-group deletion influence	INF-002, EST-003, DIAG-003; pymixef.compare.approximation_sensitivity()
Reproducible evidence	Immutable IR, result archives, manifests, comparisons, validation bundles	ARCH-001, API-002, PERF-002, REG-001, VAL-001; Validation

```{admonition} Interpret implemented at its recorded scope

class:

note

implemented: true means that the specifically named and scoped path exists and has the evidence recorded below. It does not mean that the capability is stable, reference-validated, suitable at every scale, or qualified for a regulated workflow. Conversely, implemented: false can coexist with source files or research helpers: the named integrated capability remains gated until its stated evidence and workflow requirements are satisfied.

## Installation, representation, and execution are different

**Installation** answers whether the PyMixEF distribution and its selected
dependencies are present in an environment. Installing a module, command,
schema, or helper does not change a capability's evidence state.

**Representability** answers whether a construct can be expressed in ModelIR,
the formula layer, or the pharmacometrics DSL. A represented construct may
still be rejected by a selected engine. For example, the registry explicitly
records that not every distributional predictor represented by the IR is
executable by the initial Laplace backend; mixture weights and priors can also
be represented while their population-fitting or cross-sampler validation
gates remain open.

**Execution** answers whether a selected backend can perform the requested
calculation. The **evidence registry** goes further: it records the scoped
implementation state, maturity, reproducibility class, evidence, and open
gates. Check all four questions before relying on an analysis.

## Reading the fields

| Field | Meaning |
|---|---|
| Implemented | Exact registry Boolean. `true` is scoped to the named capability; `false` is an active evidence gate. |
| Stage | Program area used to organize delivery and evidence. It is not a maturity claim. |
| Maturity | Evidence tier. Every entry in this catalog is currently `experimental`. |
| Reproducibility | `bitwise`, `deterministic-with-tolerance`, `stochastic-with-monte-carlo-error`, or `None` when no executable guarantee is assigned. |
| Evidence summary | Exact test, source, schema, benchmark, or wrapper artifacts recorded in the registry. |
| Limitations / open gates | Scope boundary or remaining work recorded by the registry. |

Current reproducibility-class counts are:

- `bitwise`: 19;
- `deterministic-with-tolerance`: 21;
- `stochastic-with-monte-carlo-error`: 2; and
- no assigned class (`None`): 14.

## Query the live registry

From the command line:

```console
pymixef capabilities
pymixef capabilities --json

From Python:

from pymixef import iter_capabilities

for capability in iter_capabilities(implemented=False):
    print(capability.identifier, capability.name)
    for gate in capability.limitations:
        print("  -", gate)

The CLI JSON form is best for automation. This page is optimized for reading, browsing, and full-text search.

Area overview

Area	Registry prefix	Total	Implemented	Gated
Architecture and backend contracts	ARCH-*	3	2	1
Public API and data behavior	API-*	3	3	0
Covariance	COV-*	2	2	0
Data and event semantics	DATA-*	3	3	0
Distribution contracts	DIST-*	3	3	0
Linear mixed models	LMM-*	3	1	2
Generalized mixed models	GLMM-*	4	2	2
Clinical longitudinal MMRM	MMRM-*	3	3	0
ODE integration	ODE-*	3	2	1
Nonlinear mixed effects	NLME-*	5	4	1
Population stochastic-approximation estimation	standalone ID	1	0	1
Estimation and convergence	EST-*	3	2	1
Inference	INF-*	2	2	0
Diagnostics	DIAG-*	3	3	0
Interoperability	INT-*	2	1	1
Advanced engines	ADV-*	3	0	3
Performance and reproducibility	PERF-*	3	2	1
Regulated workflow support	REG-*	2	2	0
Validation evidence	VAL-*	3	3	0
User experience and warnings	UX-*	2	2	0

Complete registry

Expand any record for its exact machine-readable fields and evidence notes.

Architecture and backend contracts

Immutable model semantics, estimator compatibility, and shared backend conformance.

ARCH-001 — Versioned immutable model IR

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_ir.py

• Schema evidence: src/pymixef/schemas/model-ir-v1.json

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

ARCH-002 — Estimator compatibility validation

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_model.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

ARCH-003 — Full backend conformance suite

Registry record

Registry field	Exact value
Implemented	false
Stage	foundation
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

• Implementation evidence: src/pymixef/backends/base.py

• Test evidence: tests/test_backend_conformance.py

Limitations / open gates

• A reusable fit-payload suite covers every built-in backend and checks validation, deterministic repeat fitting, input immutability, and row alignment. Objective, gradient, optional Hessian-vector product, and simulation contracts are planned additions to the Backend Protocol.

Public API and data behavior

Dry-run planning, result archival, and explicit row auditing.

API-001 — Dry-run compile, validate, and explain

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_formula.py

• Test evidence: tests/test_model.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

API-002 — Stable result archival

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_results.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

API-003 — Data audit without silent mutation

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_data_covariance.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

Covariance

Positive-definite parameterization and singularity reporting.

COV-001 — Positive-definite covariance parameterizations

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_data_covariance.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

COV-002 — Covariance singularity reporting

Registry record

Registry field	Exact value
Implemented	true
Stage	classical-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_lmm.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

Data and event semantics

Reason-coded missingness plus canonical pharmacometric event records and ordering.

DATA-001 — Canonical pharmacometric event records

Registry record

Registry field	Exact value
Implemented	true
Stage	nlme-foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_events.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

DATA-002 — Deterministic same-time event ordering

Registry record

Registry field	Exact value
Implemented	true
Stage	nlme-foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_events.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

DATA-003 — Missingness contract and reason-coded audit

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_data_covariance.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

Distribution contracts

Likelihood normalization, family behavior, and distributional predictor representation.

DIST-001 — Normalized likelihood policy

Registry record

Registry field	Exact value
Implemented	true
Stage	generalized-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_families.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

DIST-002 — Family derivatives/CDF/simulation contract

Registry record

Registry field	Exact value
Implemented	true
Stage	generalized-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_families.py

Limitations / open gates

• Analytic derivatives vary by family; finite-difference backend is used otherwise.

DIST-003 — Distributional predictor representation

Registry record

Registry field	Exact value
Implemented	true
Stage	generalized-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_ir.py

• Test evidence: tests/test_families.py

Limitations / open gates

• Not every represented predictor is executable by the initial Laplace backend.

Linear mixed models

Gaussian ML/REML execution, scale targets, and inference gates.

LMM-001 — Gaussian LMM ML/REML reference engine

Registry record

Registry field	Exact value
Implemented	true
Stage	classical-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_lmm.py

• Benchmark evidence: benchmarks/b01_sleepstudy.json

Limitations / open gates

• The reference engine uses dense marginal covariance; large-scale sparse execution requires the planned compiled backend.

LMM-002 — Sparse million-row LMM engine

Registry record

Registry field	Exact value
Implemented	false
Stage	classical-core
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• Requires the planned compiled sparse backend.

LMM-003 — Profile, bootstrap, and robust LMM inference

Registry record

Registry field	Exact value
Implemented	false
Stage	classical-core
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• A generic restartable bootstrap exists; profile and sandwich paths remain gated.

Generalized mixed models

Laplace execution, approximation gates, separation diagnostics, and parity targets.

GLMM-001 — Laplace GLMM

Registry record

Registry field	Exact value
Implemented	true
Stage	generalized-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_glmm_mmrm.py

Limitations / open gates

• Initial implementation targets documented low-dimensional random blocks.

GLMM-002 — Adaptive Gauss-Hermite quadrature

Registry record

Registry field	Exact value
Implemented	false
Stage	generalized-core
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• Compatibility validation rejects AGHQ until an order-sensitivity suite lands.

GLMM-003 — Binary separation indicator

Registry record

Registry field	Exact value
Implemented	true
Stage	generalized-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_glmm_mmrm.py

Limitations / open gates

• The indicator is a heuristic for Bernoulli/binomial fits; calibrated rare-event diagnostics and recovery benchmarks remain gated.

GLMM-004 — glmmTMB Salamanders parity

Registry record

Registry field	Exact value
Implemented	false
Stage	generalized-core
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• Zero-inflated NB2 parity remains a later evidence gate.

Clinical longitudinal MMRM

Repeated-measures covariance, inference labels, and missing-data sensitivity gates.

MMRM-001 — MMRM REML with Satterthwaite and labeled approximate KR

Registry record

Registry field	Exact value
Implemented	true
Stage	clinical-longitudinal
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_glmm_mmrm.py

Limitations / open gates

• The dense reference path is intended for small problems. Exact Kenward-Roger is rejected; only a clearly labeled approximation is available.

MMRM-002 — MMRM covariance construction checks

Registry record

Registry field	Exact value
Implemented	true
Stage	clinical-longitudinal
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_data_covariance.py

• Test evidence: tests/test_glmm_mmrm.py

Limitations / open gates

• Evidence covers positive-definite construction and local pathology tests, not external-software covariance-estimate conformance.

MMRM-003 — Missing-data sensitivity transformations

Registry record

Registry field	Exact value
Implemented	true
Stage	clinical-longitudinal
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_sensitivity_workflows.py

Limitations / open gates

• Applies audited additive response-scale deltas to explicitly identified, already-imputed cells. It does not perform imputation or choose clinically justified sensitivity deltas.

ODE integration

Reference integration, dose/event semantics, and sensitivity-validation gates.

ODE-001 — Reference ODE integration

Registry record

Registry field	Exact value
Implemented	true
Stage	nlme-foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_ode_pk.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

ODE-002 — Dose/event-aware ODE integration

Registry record

Registry field	Exact value
Implemented	true
Stage	nlme-foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_events.py

• Test evidence: tests/test_ode_pk.py

Limitations / open gates

• Covers the documented bolus, infusion, reset, and ADDL reference subset; steady-state event semantics are rejected.

ODE-003 — Independently validated ODE sensitivities

Registry record

Registry field	Exact value
Implemented	false
Stage	nlme-foundation
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

• Implementation evidence: src/pymixef/pharmacometrics/ode.py

• Test evidence: tests/test_ode_pk.py

Limitations / open gates

• Forward and central finite-difference diagnostics plus one analytic decay check exist. Analytic/automatic sensitivities, event-discontinuity cases, and an independent multi-model validation suite do not.

Nonlinear mixed effects

Conditional objectives, transforms, censoring, interoccasion variability, and mixture estimation.

NLME-001 — FOCEI-oriented conditional objective building blocks

Registry record

Registry field	Exact value
Implemented	true
Stage	nlme-foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_pharmacometrics_dsl.py

Limitations / open gates

• Provides subject objectives, conditional modes, and Laplace aggregation only. fit_focei() deliberately rejects because no integrated population optimizer or FitResult path exists.

NLME-002 — Population parameter transforms

Registry record

Registry field	Exact value
Implemented	true
Stage	nlme-foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_ir.py

• Test evidence: tests/test_pharmacometrics_dsl.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

NLME-003 — Stable BQL/censoring likelihoods

Registry record

Registry field	Exact value
Implemented	true
Stage	pharmacometrics-breadth
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_ode_pk.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

NLME-004 — Interoccasion event representation

Registry record

Registry field	Exact value
Implemented	true
Stage	pharmacometrics-breadth
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_events.py

Limitations / open gates

• Population IOV estimation remains part of the production FOCEI gate.

NLME-005 — Finite-mixture population estimation

Registry record

Registry field	Exact value
Implemented	false
Stage	pharmacometrics-breadth
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• Mixture weights can be represented; label-stable population fitting is unavailable.

Population stochastic-approximation estimation

The standalone population-estimation gate recorded by the registry.

SAEM — Integrated SAEM population estimator

Registry record

Registry field	Exact value
Implemented	false
Stage	pharmacometrics-breadth
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

• Implementation evidence: src/pymixef/pharmacometrics/estimation.py

• Test evidence: tests/test_pharmacometrics_dsl.py

Limitations / open gates

• A callback-driven research kernel exists, but it is not connected to ModelIR, event/error-model compilation, population diagnostics, or the stable FitResult contract.

Estimation and convergence

Unified convergence reporting, derivative verification, and approximation sensitivity.

EST-001 — Unified convergence object

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_results.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

EST-002 — Independent derivative verification suite

Registry record

Registry field	Exact value
Implemented	false
Stage	foundation
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

• Implementation evidence: src/pymixef/backends/base.py

• Implementation evidence: src/pymixef/pharmacometrics/estimation.py

• Test evidence: tests/test_pharmacometrics_dsl.py

• Test evidence: tests/test_lmm.py

Limitations / open gates

• Finite-difference helpers are used for optimization diagnostics, but no separate derivative implementation and systematic cross-engine verification suite is available.

EST-003 — Approximation sensitivity workflow

Registry record

Registry field	Exact value
Implemented	true
Stage	generalized-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_sensitivity_workflows.py

Limitations / open gates

• Deep-copied callback refits compare aligned parameters, covariance-derived standard errors, objectives, and caller-declared materiality flags. Scenario selection and thresholds are analyst-supplied and archived.

Inference

Uncertainty provenance and restartable bootstrap support.

INF-001 — Uncertainty provenance

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_results.py

• Implementation evidence: src/pymixef/reporting.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

INF-002 — Restartable bootstrap with failure accounting

Registry record

Registry field	Exact value
Implemented	true
Stage	classical-core
Maturity	experimental
Reproducibility	stochastic-with-monte-carlo-error

Evidence summary

• Test evidence: tests/test_random_inference.py

Limitations / open gates

• This is a generic callback/cluster bootstrap helper, not yet an integrated profile, BCa, or model-specific inference workflow.

Diagnostics

Data-first diagnostics, VPC calculations, and influence-analysis gates.

DIAG-001 — Diagnostic data-first contract

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_results.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

DIAG-002 — VPC calculations

Registry record

Registry field	Exact value
Implemented	true
Stage	pharmacometrics-breadth
Maturity	experimental
Reproducibility	stochastic-with-monte-carlo-error

Evidence summary

• Test evidence: tests/test_results.py

Limitations / open gates

• The 0.1 helper computes binned VPC tables from supplied simulations; it does not provide an integrated NLME simulation/refit workflow.

DIAG-003 — Grouping-safe influence analysis

Registry record

Registry field	Exact value
Implemented	true
Stage	classical-core
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_sensitivity_workflows.py

Limitations / open gates

• Full delete-group refits require archived fixed_effect_rank; Cook-style distance requires a finite symmetric positive-semidefinite baseline covariance. Optional approximations are reported beside, never substituted for, the full refit.

Interoperability

Machine-readable compatibility reporting and the release-gated R wrapper.

INT-001 — Machine-readable compatibility reports

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_interoperability.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

INT-002 — Release-gated thin R wrapper

Registry record

Registry field	Exact value
Implemented	false
Stage	foundation
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

• R-package evidence: r/pymixef/R/pymixef.R

• R-package evidence: r/pymixef/man/pymixef-package.Rd

• R-package evidence: r/pymixef/tests/testthat/test-wrapper-api.R

• R-package evidence: r/pymixef/tests/testthat/test-python-parity.R

Limitations / open gates

• The alpha reticulate package has Rd files and mocked/live testthat suites, R CMD build succeeds, and a dependency-complete local R CMD check –no-manual passes. No cross-platform R CI gate exists, and the maintainer address is an explicit non-routable placeholder.

Advanced engines

Priors, robust sensitivity, and joint longitudinal-event targets.

ADV-001 — Backend-neutral priors exported to two samplers

Registry record

Registry field	Exact value
Implemented	false
Stage	advanced-engines
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• Priors are represented in the IR; two-backend equivalence is not yet validated.

ADV-002 — Robust sensitivity comparison

Registry record

Registry field	Exact value
Implemented	false
Stage	advanced-engines
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• No robust-likelihood fit path or automated cross-model sensitivity report is implemented.

ADV-003 — Joint longitudinal-event simulation

Registry record

Registry field	Exact value
Implemented	false
Stage	advanced-engines
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

No completed evidence artifact is recorded for this gated capability.

Limitations / open gates

• No joint longitudinal/event model, shared random-effect simulator, or validated event-time likelihood is implemented.

Performance and reproducibility

Benchmarking, reproducibility declarations, and numerical thread controls.

PERF-001 — Reduced JSON benchmark harness

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Benchmark evidence: benchmarks/run.py

Limitations / open gates

• The current harness contains one reduced synthetic LMM workload; broader cross-platform performance coverage is planned.

PERF-002 — Declared reproducibility classes

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_provenance.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

PERF-003 — Explicit numerical thread controls

Registry record

Registry field	Exact value
Implemented	false
Stage	foundation
Maturity	experimental
Reproducibility	None (no class assigned)

Evidence summary

• Implementation evidence: src/pymixef/provenance.py

• Test evidence: tests/test_provenance.py

Limitations / open gates

• Run manifests record ambient thread-related environment variables, but PyMixEF does not configure or enforce numerical library thread counts.

Regulated workflow support

Validation-bundle and change-impact evidence helpers.

REG-001 — Validation bundle generator

Registry record

Registry field	Exact value
Implemented	true
Stage	regulated-workflow-support
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_validation.py

Limitations / open gates

• Supports evidence generation; it is not a universal validation claim.

REG-002 — Change-impact classification

Registry record

Registry field	Exact value
Implemented	true
Stage	regulated-workflow-support
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_validation.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

Validation evidence

Traceability, selected references, and the initial pathology corpus.

VAL-001 — Public traceability matrix

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_validation.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

VAL-002 — Selected independent reference calculations

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_families.py

Limitations / open gates

• External reference checks currently cover selected normalized family likelihoods; they are not independent full-engine or cross-software parity reports.

VAL-003 — Initial failure and pathology corpus

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	deterministic-with-tolerance

Evidence summary

• Test evidence: tests/test_formula.py

• Test evidence: tests/test_data_covariance.py

• Test evidence: tests/test_glmm_mmrm.py

Limitations / open gates

• The initial corpus covers representative parser, covariance, and engine failures; broad adversarial and platform-specific cases remain future work.

User experience and warnings

Stable warning semantics and deterministic model comparison.

UX-001 — Stable warning catalog

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Implementation evidence: src/pymixef/warning_catalog.json

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

UX-002 — Deterministic model diff

Registry record

Registry field	Exact value
Implemented	true
Stage	foundation
Maturity	experimental
Reproducibility	bitwise

Evidence summary

• Test evidence: tests/test_ir.py

Limitations / open gates

No capability-specific limitation or open gate is recorded in the registry.

Catalog-wide interpretation

A constructive way to use this catalog

Start with the scientific method you need, confirm that its scoped capability is implemented, then read its reproducibility class, evidence, and open gates together. For an unimplemented entry, the gate describes a concrete path to stronger coverage rather than an invitation to bypass compatibility checks. For an implemented experimental entry, use the evidence as a starting point for design-specific verification and sensitivity work.