Analysis matrix

This matrix distinguishes what the 0.1 reference paths calculate from what the broader object catalog can represent.

Fitted statistical models

Analysis	Response	Dependence	Engine/method	Principal outputs	Current boundary
Gaussian LMM	continuous	one or more Gaussian random blocks; supported residual structures	lmm, ML/REML	fixed effects/covariance, natural-scale variance components, modes, conditional/population fitted values, residuals, simulation	dense experimental reference path for small/moderate problems; not compiled sparse scale
Bernoulli GLMM	binary	Gaussian random blocks	glmm, first-order Laplace	link-scale coefficients, conditional ORs after transformation, conditional modes, fitted probabilities, convergence/approximation metadata	canonical logit reference path; no AGHQ
Binomial GLMM	successes/trials	Gaussian random blocks	glmm, first-order Laplace	conditional log-odds effects, modes, fitted means	supported parameterization must validate
Poisson GLMM	counts	Gaussian random blocks	glmm, first-order Laplace	conditional log-rate effects, modes, fitted counts	exposure/offset meaning is analyst responsibility
NB2 GLMM	overdispersed counts	Gaussian random blocks	glmm, first-order Laplace	conditional mean and NB2 dispersion path	NB1 and other catalog families are not fit by this engine
MMRM	continuous repeated visits	dense within-subject residual covariance, between-subject independence	mmrm, ML/REML	fixed effects/covariance, visit covariance, linear inference, labeled DF, exclusions/visit order	no simultaneous formula random effects; exact KR is not claimed

MMRM covariance choices

Structure	Heterogeneous variance	Lag/distance behavior	Ordering requirement
Diagonal	optional by declaration	no off-diagonal correlation	visit identity
Unstructured	yes	every covariance free	explicit visit axis
Compound symmetry	no	common correlation	visit identity
AR(1)	no	geometric decay by ordered lag	ordered visits
Heterogeneous AR(1)	yes	geometric decay by ordered lag	ordered visits
Toeplitz	no	separate correlation per lag	ordered visits
Heterogeneous Toeplitz	yes	separate correlation per lag	ordered visits
Ante-dependence	yes/flexible	sequential conditional dependence	ordered visits
Spatial power	structure-defined	numeric distance	numeric visit times/distances
Known covariance	supplied	supplied	axis must match matrix

Pharmacometric calculations

Task	Entry points	Output	Estimation?
Canonical event preparation	canonicalize_events, EventTable expansion methods	immutable event table + row/action audit	no
One-compartment PK	bolus, infusion, oral helpers / OneCompartmentPK	structural concentration	no
Two-compartment PK	bolus, infusion, oral helpers / TwoCompartmentPK	structural concentration and derived rates	no
Event-aware ODE	simulate_ode, simulate_subjects	state trajectories, observations, metadata, sensitivities	no
Residual-error likelihood	additive/proportional/power/combined/lognormal; censoring helpers	variance, draws, log likelihood	evaluates supplied parameters
Typed model contract	@model, declarations, CompiledModel	validated model + ModelIR	no
Conditional ETA mode	ConditionalObjective, find_conditional_mode	mode, objective components, gradient/Hessian/covariance evidence	subject-level primitive
Population Laplace aggregation	laplace_population_objective	subject contributions/modes + objective	primitive, not integrated outer fit
FOCEI	fit_focei	explicit UnsupportedEstimatorError	unavailable
SAEM	experimental_saem / saem	callback-driven research-kernel result	experimental primitive, not integrated estimator

Post-fit analysis

Need	API	Key contract
Human summary	FitResult.summary()	readable view; structured fields remain authoritative
Prediction	FitResult.prediction(mode=...)	mode is explicit
Residual diagnostics	residual_diagnostics, residual_table	machine-readable row-aligned table
Named diagnostics	FitResult.diagnostic	returns DiagnosticTable
Simulation	FitResult.simulate	seed and included uncertainty sources recorded
VPC	FitResult.vpc, vpc_table	bin/quantile data, not a pass/fail certificate
Bootstrap	bootstrap	row/cluster resampling, checkpoint/resume, failure accounting
Linear inference	MMRM linear_inference	explicit coefficient mapping and DF label
External comparison	compare	parameter mapping and convention matching
Report	render_report	Markdown/HTML; optional PDF/Word dependencies
Archive	FitResult.save/load	inspectable JSON + integrity sidecar
Validation bundle	create/verify helpers	evidence package; data excluded by default

Representable but not automatically fitted

The family catalog includes Student-t, lognormal, gamma, inverse Gaussian, beta, Tweedie, NB1, generalized Poisson, COM-Poisson, ordinal, multinomial, zero-inflated, hurdle, truncated, censored, and survival objects. ModelIR can represent priors, distributional predictors, and richer typed nodes. Representation supports calculation, exchange, or future backend contracts; it does not imply that a current formula backend can estimate the model.

Unavailable integrated paths

The 0.1 release does not provide:

• adaptive Gauss–Hermite quadrature;

• a production FOCEI or integrated SAEM population estimator;

• exact Kenward–Roger inference;

• a compiled sparse million-row LMM backend;

• finite-mixture estimation;

• advanced MCMC/samplers;

• joint longitudinal-event estimation;

• automatic missing-data sensitivity recipes;

• universal external-format translation or regulatory qualification.

Unsupported requests are validated and refused rather than silently replaced. For machine-readable release state, run pymixef capabilities --json.