Glossary

Analysis row

An input row retained in the likelihood after the explicit data and missingness contract is applied.

Approximation

A named numerical/statistical replacement for an exact calculation, such as first-order Laplace integration around a conditional mode.

Boundary estimate

A fitted parameter at or numerically near the edge of its admissible space, such as a variance near zero or correlation near ±1.

Canonical event

A normalized pharmacometric record with explicit event type, deterministic same-time priority, amount semantics, row identity, and source provenance.

Conditional mode

The mode of a group/subject random effect given observations and population parameters; often called an empirical Bayes mode. It is estimated and shrunk, not directly observed.

Conditional prediction

Prediction that includes fitted random effects for observed groups.

Covariance axis

The ordered visits or times to which a structured repeated-measures covariance matrix refers.

Data audit

One disposition per source row plus factor, transformation, and fingerprint information connecting input data to analysis data.

Degrees of freedom (DF)

A named reference distribution/calculation used for finite-sample inference. PyMixEF preserves labels such as residual or Satterthwaite delta-method.

Engine

A numerical backend that consumes a compatible compiled model/data payload, such as lmm, glmm, or mmrm.

Estimand

The precisely defined quantity an analysis aims to estimate, including population, endpoint, treatment condition, handling of intercurrent events, and summary measure where relevant.

Family

A conditional response distribution and its probability contract.

Fixed effect

A population-level coefficient in the linear predictor.

FOCEI

First-order conditional estimation with interaction. The integrated production estimator is not available in PyMixEF 0.1.

GLMM

Generalized linear mixed model: a non-Gaussian response model with a link function and random effects.

Integrity hash

A cryptographic digest used to detect content change. It is not a digital signature or scientific-validity assessment.

Laplace approximation

Approximation of an integral from local curvature around a mode. The current GLMM path uses a first-order Laplace approximation.

A transformation connecting a conditional response mean to the linear predictor.

LMM

Linear mixed model: a Gaussian response model with fixed and random effects.

Manifest

Structured provenance describing software, environment, engine/method, options, warnings, timing, and model/data fingerprints.

Marginal prediction

A prediction integrated over random effects. It is not generally identical to setting the random effect to zero in a nonlinear-link model.

MMRM

Mixed model for repeated measures: in this documentation, a Gaussian longitudinal model with explicit within-subject residual covariance and between-subject independence.

ModelIR

PyMixEF’s immutable, typed, versioned, backend-neutral model intermediate representation.

Population prediction

Fixed-effect or typical-group prediction with random effects at their reference value. In a nonlinear GLMM this is conditional-at-zero, not necessarily random-effect-integrated marginal prediction.

Random effect

A latent group-specific deviation drawn from a declared covariance model.

REML

Restricted maximum likelihood, which integrates/adjusts for fixed effects under a stated constant convention when estimating Gaussian covariance parameters.

Reproducibility class

Manifest label stating whether a computation is deterministic, deterministic within numerical tolerance, or stochastic with a recorded seed/Monte Carlo error.

Residual

Observed minus fitted response under a named prediction/variance convention.

SAEM

Stochastic approximation expectation maximization. PyMixEF 0.1 exposes a callback-driven experimental research kernel, not an integrated population estimator.

Semantic hash

Deterministic hash of model meaning after canonical serialization, excluding nonsemantic formatting.

Source row

Original input record identified by source position/index and a stable row ID.

Trustworthy convergence

The package’s combined interpretation gate over optimizer termination, gradients, curvature/boundaries, inner modes, and warning state.

Validation bundle

Deterministic collection of result, manifest, traceability, hashes, and optionally authorized data that supports a context-specific validation process.

Visual predictive check (VPC)

Comparison of observed summaries with distributions of analogous summaries from seeded model simulations, returned by PyMixEF as auditable table data.