pymixef.backends package

Built-in SciPy/NumPy reference estimation backends.

class pymixef.backends.Backend(*args, **kwargs)

Bases: Protocol

Protocol implemented by all estimation engines.

fit(data, **options)

Fit a compiled numeric model and return the shared payload.

Parameters:

• data (Any)

• options (Any)

Return type:

dict[str, Any]

name: str

exception pymixef.backends.BackendError(message, *, code='BACKEND-001', details=None, suggested_engines=())

Bases: PyMixEFError

A stable, machine-readable backend input or numerical error.

Parameters:

• message (str)

• code (str)

• details (Mapping[str, Any] | None)

• suggested_engines (Sequence[str])

Return type:

None

to_dict()

Return a JSON-compatible diagnostic record.

Return type:

dict[str, Any]

exception pymixef.backends.BackendInputError(message, *, details=None)

Bases: BackendError

The compiled numeric input is invalid.

Parameters:

• message (str)

• details (Mapping[str, Any] | None)

Return type:

None

exception pymixef.backends.BackendNumericalError(message, *, details=None)

Bases: BackendError

A numerical calculation failed before a valid payload could be made.

Parameters:

• message (str)

• details (Mapping[str, Any] | None)

Return type:

None

exception pymixef.backends.BackendUnsupportedError(message, *, details=None, suggested_engines=())

Bases: EngineCompatibilityError

The requested model is scientifically unsupported by this engine.

Parameters:

• message (str)

• details (Mapping[str, Any] | None)

• suggested_engines (Sequence[str])

Return type:

None

to_dict()

Return a JSON-compatible diagnostic record.

Return type:

dict[str, Any]

pymixef.backends.DenseLMMBackend

alias of GaussianLMMBackend

pymixef.backends.GLMMBackend

alias of LaplaceGLMMBackend

class pymixef.backends.GaussianLMMBackend

Bases: object

Exact dense/reference Gaussian LMM engine.

fit(data, *, method=None, reml=None, maxiter=1_000, tolerance=1e-8, compute_hessian=True, **options)

Parameters:

• data (Any)

• method (str | None)

• reml (bool | None)

• maxiter (int)

• tolerance (float)

• compute_hessian (bool)

• options (Any)

Return type:

dict[str, Any]

name = 'lmm'

pymixef.backends.LMMBackend

alias of GaussianLMMBackend

class pymixef.backends.LaplaceGLMMBackend

Bases: object

Dense grouped-random-effect Laplace GLMM reference backend.

fit(data, *, family=None, maxiter=500, tolerance=1e-7, inner_maxiter=100, inner_tolerance=1e-9, compute_hessian=True, **options)

Parameters:

• data (Any)

• family (Any)

• maxiter (int)

• tolerance (float)

• inner_maxiter (int)

• inner_tolerance (float)

• compute_hessian (bool)

• options (Any)

Return type:

dict[str, Any]

name = 'laplace'

class pymixef.backends.MMRMBackend

Bases: object

Dense subject-block MMRM REML engine.

fit(data, *, covariance=None, method='REML', df_method='satterthwaite', confidence_level=0.95, maxiter=1_000, tolerance=1e-8, compute_hessian=True, **options)

Parameters:

• data (Any)

• covariance (Any)

• method (str)

• df_method (str)

• confidence_level (float)

• maxiter (int)

• tolerance (float)

• compute_hessian (bool)

• options (Any)

Return type:

dict[str, Any]

name = 'mmrm'

pymixef.backends.fit_glmm(data, **options)

Fit a supported GLMM by dense first-order Laplace approximation.

Parameters:

• data (Any)

• options (Any)

Return type:

dict[str, Any]

pymixef.backends.fit_lmm(data, **options)

Fit a Gaussian LMM with the dense reference backend.

Parameters:

• data (Any)

• options (Any)

Return type:

dict[str, Any]

pymixef.backends.fit_mmrm(data, **options)

Fit a Gaussian MMRM with dense subject covariance blocks.

Parameters:

• data (Any)

• options (Any)

Return type:

dict[str, Any]

pymixef.backends.get_backend(name)

Instantiate a built-in backend by stable engine name.

Parameters:

name (str)

Return type:

Backend

pymixef.backends.validate_payload(payload)

Validate the backend-neutral result contract and return canonical key order.

The validator is shared by built-in and third-party backends. It checks presence, numeric finiteness, observation alignment, covariance shape, convergence state, and container types without imposing an engine-specific parameterization.

Parameters:

payload (Mapping[str, Any])

Return type:

dict[str, Any]

Submodules

• pymixef.backends.base module
  • Backend
    • Backend.name
    • Backend.fit()
  • BackendError
    • BackendError.to_dict()
  • BackendInputError
  • BackendNumericalError
  • BackendUnsupportedError
    • BackendUnsupportedError.to_dict()
  • CompiledData
    • CompiledData.source
    • CompiledData.y
    • CompiledData.X
    • CompiledData.fixed_names
    • CompiledData.random_blocks
    • CompiledData.offset
    • CompiledData.weights
    • CompiledData.trials
    • CompiledData.residual_covariance
    • CompiledData.residual_covariance_fixed
    • CompiledData.n_obs
    • CompiledData.n_fixed
    • CompiledData.random_design
  • CovarianceParameterization
    • CovarianceParameterization.block
    • CovarianceParameterization.size
    • CovarianceParameterization.initial()
    • CovarianceParameterization.matrix()
    • CovarianceParameterization.expanded_matrix()
    • CovarianceParameterization.names_and_values()
    • CovarianceParameterization.unconstrained_names()
  • RandomBlockData
    • RandomBlockData.name
    • RandomBlockData.row_design
    • RandomBlockData.design
    • RandomBlockData.group_codes
    • RandomBlockData.group_levels
    • RandomBlockData.term_names
    • RandomBlockData.correlated
    • RandomBlockData.terms_per_group
    • RandomBlockData.n_groups
    • RandomBlockData.coefficient_count
    • RandomBlockData.coefficient_labels()
  • backend_mapping()
  • cho_solve()
  • convergence_mapping()
  • covariance_from_hessian()
  • covariance_slices()
  • factorize()
  • field()
  • finite_gradient()
  • finite_hessian()
  • logdet_from_cholesky()
  • make_payload()
  • optimizer_covariance()
  • prepare_data()
  • prepare_random_block()
  • random_covariance()
  • safe_cholesky()
  • select_optimizer_result()
  • validate_payload()
• pymixef.backends.glmm module
  • GLMMBackend
  • LaplaceGLMMBackend
    • LaplaceGLMMBackend.name
    • LaplaceGLMMBackend.fit()
  • fit_glmm()
• pymixef.backends.lmm module
  • DenseLMMBackend
  • GaussianLMMBackend
    • GaussianLMMBackend.name
    • GaussianLMMBackend.fit()
  • LMMBackend
  • fit_lmm()
• pymixef.backends.mmrm module
  • MMRMBackend
    • MMRMBackend.name
    • MMRMBackend.fit()
  • contrasts()
  • estimated_marginal_means()
  • fit_mmrm()
  • linear_inference()