API reference

This is the task-oriented entrance to the complete PyMixEF Python API. Every public module is linked to its generated reference page, while the tables name the classes, functions, constants, and protocols that readers are most likely to search for directly.

Use the generated module index when you already know the import path. Use the alias reference when code uses a convenience name such as pymixef.load, Normal, NB2, dry_run, or estimated_marginal_means.

Find an API by task

Goal	Start here	Principal modules
Fit or inspect a model	1. Fit, compile, validate, and explain a model	pymixef, pymixef.model
Parse a formula or build design matrices	2. Formulas and design matrices	pymixef.formula
Normalize, audit, or reconcile data	3. Data adaptation, missingness, and audit	pymixef.data
Run LMM, GLMM, or MMRM calculations	4. LMM, GLMM, and MMRM backends	pymixef.backends.*
Calculate probability distributions	5. Probability families and links	pymixef.families
Declare covariance or parameter constraints	6. Covariance structures and parameter transforms	pymixef.covariance, pymixef.transforms
Work with fit results, diagnostics, or uncertainty	7. Results, diagnostics, simulation, inference, and reporting	pymixef.results, pymixef.diagnostics, pymixef.inference
Author a pharmacometric model	8. Pharmacometric model authoring	pymixef.pharmacometrics.dsl
Process dosing events, PK, or ODE systems	9. Events, closed-form PK, ODE simulation, and population primitives	pymixef.pharmacometrics.events, .pk, .ode, .estimation
Inspect or migrate the model IR	10. Model IR, schema migration, hashing, and diffing	pymixef.ir
Translate external formats	11. Interoperability and compatibility accounting	pymixef.interoperability.*
Capture evidence and reproducibility	12. Capabilities, provenance, randomness, and validation evidence	pymixef.capabilities, .provenance, .random, .validation
Register extensions	13. Plugins and advanced backend contracts	pymixef.plugins, pymixef.backends.base
Handle errors, warnings, native discovery, or the CLI	14. Errors, warnings, native discovery, and command line	pymixef.errors, .warnings, .native, .cli

Root exports and submodule APIs

The pymixef root module is the concise everyday surface. It exports fit, Model, ModelIR, FitResult, compare, bootstrap, render_report, capability and validation helpers, convergence types, stable exception types, and the families, covariance, diagnostics, interoperability, and pharmacometrics namespaces.

Specialized APIs remain in their owning modules:

import pymixef
from pymixef.data import audit_data
from pymixef.formula import compile_formula
from pymixef.pharmacometrics import canonicalize_events, simulate_ode

result = pymixef.fit("response ~ time + (1 | subject)", data=data)
audited = audit_data(data, response="response", covariates=("time",))
matrices = compile_formula("response ~ time", data)

An object can therefore be public without being a direct pymixef.<name> export. For example, DiagnosticTable belongs to pymixef.diagnostics, Capability belongs to pymixef.capabilities, and CompatibilityReport belongs to pymixef.interoperability. Prefer the documented owning module for stable imports. See API alias reference for names that deliberately resolve to another public object.

Important

Catalog availability is not estimator support The family, covariance, IR, and pharmacometric catalogs are broader than the formula backends that can execute a complete fit.

• Formula LMM execution is Gaussian ML or REML.

• Formula GLMM execution is the documented Bernoulli, binomial, Poisson, and negative-binomial-2 Laplace subset.

• The dedicated MMRM path is Gaussian REML with an explicit residual covariance.

• Other Family classes remain useful for normalized likelihood, probability, moment, and simulation calculations, but their presence does not make them executable by pymixef.fit.

• Priors and distributional predictors can be represented in ModelIR; current formula backends do not execute those declarations.

• fit_focei() deliberately rejects because an integrated production FOCEI path is not present. experimental_saem() is a callback-driven experimental kernel rather than an integrated population estimator.

Check pymixef.iter_capabilities() or pymixef capabilities --json before depending on an experimental calculation path.

1. Fit, compile, validate, and explain a model

Module	Public objects
pymixef	fit, Model, Response, Fixed, Random, ExecutionPlan, FitResult, load
pymixef.model	Classes Response, Fixed, Random, ValidationFinding, ValidationReport, Model, ExecutionPlan; function fit

Model.from_formula() creates a backend-neutral model. Model.validate(), Model.explain(), and Model.compile() inspect compatibility before optimization. ExecutionPlan.validate(), ExecutionPlan.explain(), and ExecutionPlan.to_backend_data() expose the deterministic compiled plan; ExecutionPlan.fit() executes it. pymixef.fit() is the direct formula or prebuilt-Model entry point.

2. Formulas and design matrices

Module	Public classes	Public functions
pymixef.formula	RandomTerm, FormulaSpec, RandomDesignBlock, FormulaExplanation, DesignMatrices	parse_formula, compile_formula, dry_run, model_matrix, explain_formula

Search for FormulaSpec.to_ir, FormulaSpec.explain, DesignMatrices.to_backend_data, DesignMatrices.explanation, and DesignMatrices.explain when inspecting compilation. Formula expressions support documented fixed effects, interactions, nesting, correlated | and independent || random blocks, categorical encoding, safe transforms, and row-complete audit metadata.

3. Data adaptation, missingness, and audit

Module	Public classes and enums	Public functions
pymixef.data	MissingnessKind, ColumnSchema, ColumnarData, DataAdapter, AuditRecord, DataAudit, AuditedData, PatternMixtureRecord, PatternMixtureResult	is_missing, missing_mask, adapt_data, InputAdapter, audit_data, prepare_data, pattern_mixture_adjust, find_duplicate_keys, validate_monotonic_time, stable_sort

ColumnarData is the immutable internal table and exposes n_rows, n_columns, column_names, take, to_dict, and fingerprint. DataAudit exposes source-to-analysis reconciliation through excluded_rows, excluded_row_ids, reason_counts, and to_dict.

4. LMM, GLMM, and MMRM backends

Module	Public API
pymixef.backends	BUILTIN_BACKENDS, Backend, BackendError, BackendInputError, BackendUnsupportedError, BackendNumericalError, GaussianLMMBackend, LMMBackend, DenseLMMBackend, LaplaceGLMMBackend, GLMMBackend, MMRMBackend, get_backend, fit_lmm, fit_glmm, fit_mmrm
pymixef.backends.lmm	GaussianLMMBackend, LMMBackend, DenseLMMBackend, fit_lmm
pymixef.backends.glmm	LaplaceGLMMBackend, GLMMBackend, fit_glmm
pymixef.backends.mmrm	MMRMBackend, fit_mmrm, linear_inference, estimated_marginal_means, contrasts
pymixef.backends.base	Backend, backend error classes, RandomBlockData, CompiledData, CovarianceParameterization, field, backend_mapping, factorize, prepare_random_block, prepare_data, covariance_slices, random_covariance, safe_cholesky, cho_solve, logdet_from_cholesky, finite_hessian, finite_gradient, covariance_from_hessian, convergence_mapping, make_payload, validate_payload

The backend functions return the lower-level payload contract. pymixef.fit() and ExecutionPlan.fit() convert that payload into a FitResult with convergence, provenance, warnings, diagnostics, and archived model semantics.

5. Probability families and links

pymixef.families defines the common Family and Link contracts.

Catalog	Public objects
Links	Link, links, get_link, IDENTITY, LOG, LOGIT, PROBIT, CLOGLOG, CAUCHIT, INVERSE, INVERSE_SQUARED
Continuous	Gaussian, StudentT, LogNormal, Gamma, InverseGaussian, Beta, Tweedie
Discrete and categorical	Bernoulli, Binomial, Poisson, NegativeBinomial1, NegativeBinomial2, GeneralizedPoisson, COMPoisson, Ordinal, Multinomial
Mixture and observation wrappers	ZeroInflated, Hurdle, Truncated, Censored
Survival	Exponential, LogNormalSurvival, Weibull, Gompertz, LogLogistic, PiecewiseExponential

The shared calculation vocabulary is log_prob, log_probability, logpdf, logpmf, cdf, logcdf, sf, logsf, rvs, random, mean, variance, and moments. The alias reference lists short names including Normal, NB1, NB2, GenPoisson, and the *Family and *Survival aliases.

6. Covariance structures and parameter transforms

Module	Public API
pymixef.covariance	CovarianceValidation, CovarianceStructure, Diagonal, Unstructured, CompoundSymmetry, AR1, HeterogeneousAR1, Toeplitz, HeterogeneousToeplitz, AnteDependence, SpatialPower, KnownCovariance, validate_covariance, covariance_structure, get_covariance, singularity_report, and the documented *Covariance aliases
pymixef.transforms	Transform, IdentityTransform, LogTransform, SoftplusTransform, BoundedTransform, SimplexTransform, OrderedTransform, CholeskyCovarianceTransform, get_transform

CovarianceStructure.parameter_count, parameter_names, covariance, matrix, validate, derivatives, simulate, and to_dict form the structure contract. Transform.forward, inverse, log_abs_det_jacobian, and jacobian form the optimizer-to-natural-scale contract.

7. Results, diagnostics, simulation, inference, and reporting

Module	Public API
pymixef.results	FitResult; properties success, n_observations; methods prediction, diagnostic, residual_diagnostics, simulate, vpc, to_dict, save, from_dict, load, summary
pymixef.diagnostics	DiagnosticTable, GroupInfluenceResult, residual_table, vpc_table, covariance_singularity_table, group_influence
pymixef.inference	BootstrapResult, bootstrap
pymixef.compare	ComparisonResult, ApproximationSensitivityResult, compare, approximation_sensitivity
pymixef.reporting	render_report
pymixef.convergence	HessianDiagnostics, BoundaryRecord, ConvergenceReport

DiagnosticTable.save() and DiagnosticTable.load() handle JSON and CSV diagnostic data. BootstrapResult.intervals() provides the documented portable percentile interval path. GroupInfluenceResult retains group-level refit and approximation failures; ApproximationSensitivityResult retains exact named settings and cross-refit failures. ComparisonResult.assert_within() and ComparisonResult.write_report() support explicit cross-platform tolerances and reports. render_report() emits Markdown, HTML, PDF, or Word according to the destination suffix and installed optional dependencies.

8. Pharmacometric model authoring

The pymixef.pharmacometrics umbrella re-exports the typed pharmacometric surface. The owning authoring module is pymixef.pharmacometrics.dsl.

Area	Public objects
Expressions and symbols	Expr, Param, Eta, State, Symbol, as_expr, symbol, covariate, exp, log, sqrt, log1p
Differential equations and observations	Dose, DifferentialEquation, derivative, d, Observation, observe
Model declarations	ValidationMessage, ModelValidation, CompiledModel, ModelDefinition, model, compiled_model
Error	DSLValidationError

Search for Param.real, Param.positive, Param.bounded, Eta.correlated, Eta.independent, Dose.into, State.derivative, Expr.evaluate, CompiledModel.to_ir, and ModelDefinition.compile for the principal builder operations.

9. Events, closed-form PK, ODE simulation, and population primitives

Module	Public API
pymixef.pharmacometrics.events	EventValidationError, EventType, DoseAmountStatus, EVENT_PRIORITY, AuditEntry, CanonicalEvent, EventTable, canonicalize_events
pymixef.pharmacometrics.pk	PKValidationError, OneCompartmentPK, TwoCompartmentPK, TwoCompartmentRates, one_compartment_iv_bolus, one_compartment_infusion, one_compartment_oral, two_compartment_rates, two_compartment_iv_bolus, two_compartment_infusion, two_compartment_oral, ObservationError, AdditiveError, ProportionalError, PowerError, CombinedError, LogNormalError, additive, proportional, power, combined, lognormal, left_censored_loglikelihood, right_censored_loglikelihood, interval_censored_loglikelihood, and PK route-name aliases
pymixef.pharmacometrics.ode	ODESimulationError, UnsupportedEventSemantics, ODEContext, EventSnapshot, ODESolverMetadata, ODESimulationResult, SensitivityCheck, simulate_ode, finite_difference_sensitivities, simulate_subjects
pymixef.pharmacometrics.estimation	EstimationError, UnsupportedEstimatorError, ConditionalModeError, SAEMError, ObjectiveComponents, ConditionalObjective, ConditionalModeResult, LaplacePopulationResult, SAEMProblem, SAEMControl, SAEMResult, apply_random_effects, omega_from_standard_deviations, eta_shrinkage, conditional_mode_objective, finite_difference_gradient, finite_difference_hessian, find_conditional_mode, laplace_population_objective, fit_focei, experimental_saem, saem

CanonicalEvent exposes event classification, effective dose/rate/duration, and record conversion. EventTable provides subject selection, source and canonical record conversion, ADDL and infusion expansion, and provenance. ODESimulationResult.state() and sensitivity() select named output arrays.

10. Model IR, schema migration, hashing, and diffing

pymixef.ir defines:

• schema constants IR_SCHEMA_VERSION, MODEL_IR_SCHEMA_VERSION, LEGACY_IR_SCHEMA_VERSION, and SUPPORTED_IR_SCHEMA_VERSIONS;

• IRNode and the typed nodes ParameterIR, FixedEffectIR, RandomEffectIR, PredictorIR, LikelihoodIR, CovarianceIR, TransformIR, PriorIR, StateEquationIR, EventIR, and OutputIR;

• the complete ModelIR graph;

• register_ir_migration, migrate_ir, diff_models, and alias model_diff;

• DiffEntry and ModelDiff.

ModelIR.to_dict(), canonical_json(), to_json(), from_dict(), from_json(), semantically_equal(), and diff() are the serialization and comparison operations. ModelIR.semantic_hash and ModelIR.hash are properties. ModelDiff.equal and ModelDiff.categories are also properties.

11. Interoperability and compatibility accounting

Module	Public API
pymixef.interoperability	CompatibilityReport, InterchangeResult, all public import, export, parse, and translation functions below
pymixef.interoperability.base	CompatibilityReport, InterchangeResult, issues
pymixef.interoperability.nonmem	parse_control_stream, import_nonmem_data, import_nonmem_table
pymixef.interoperability.pharmml	import_pharmml, export_pharmml
pymixef.interoperability.sbml	import_sbml, export_sbml
pymixef.interoperability.sedml	import_sedml, export_sedml
pymixef.interoperability.r	translate_r_formula

Every interchange function returns a value with explicit compatibility accounting. Use CompatibilityReport.supported, by_status, require_supported, to_dict, and write; use InterchangeResult.require_supported() when unsupported constructs must stop a workflow.

12. Capabilities, provenance, randomness, and validation evidence

Module	Public API
pymixef.capabilities	Capability, CAPABILITIES, get_capability, iter_capabilities
pymixef.provenance	environment_snapshot, fingerprint_data, fingerprint_model_ir, RunManifest, RunTimer
pymixef.random	RandomStreamManager, random_streams
pymixef.validation	RequirementLinks, TRACEABILITY_LINKS, TraceabilityRecord, traceability_matrix, classify_change, change_impact, create_validation_bundle, verify_validation_bundle
pymixef root	Maturity, ReproducibilityClass, WarningRecord

RunManifest.capture() records model and data fingerprints, environment, settings, seeds, reproducibility class, convergence, and warnings. RandomStreamManager.generator() and replicates() derive order-independent Philox streams. Validation bundles are created by create_validation_bundle() and checked by verify_validation_bundle().

13. Plugins and advanced backend contracts

pymixef.plugins exposes:

• PluginInfo and generic Registry;

• protocols CovariancePlugin and EstimatorPlugin;

• discover_plugins;

• FAMILY_REGISTRY, LINK_REGISTRY, COVARIANCE_REGISTRY, ESTIMATOR_REGISTRY, DIAGNOSTIC_REGISTRY, EXPORTER_REGISTRY, and ODE_SOLVER_REGISTRY.

Use Registry.register, unregister, get, info, names, and snapshot for explicit runtime registration. Low-level implementers should also consult pymixef.backends.base for the Backend protocol and compiled numeric payload contracts.

14. Errors, warnings, native discovery, and command line

Module	Public API
pymixef.errors	PyMixEFError, ValidationError, FormulaError, DataError, CovarianceError, TransformError, IRVersionError, IRValidationError, PluginError, UnsupportedCapabilityError, EngineCompatibilityError, UnsupportedEngineError, CompatibilityError
pymixef.warnings	PyMixEFWarning, DataAuditWarning, CovarianceWarning, NumericalWarning, load_warning_catalog, warning_record, emit_warning
pymixef.native	library_path, native_available, core_version
pymixef.cli	programmatic function main; console commands capabilities, traceability, explain, fit, bundle, verify-bundle, parse-nonmem
pymixef root	__version__ and root-exported error classes

Expected PyMixEF exceptions carry code, remediation, details, and source_location; use PyMixEFError.to_dict() instead of parsing display messages. Structured warnings carry a WarningRecord, and load_warning_catalog() returns the packaged stable warning catalog.