Enum FittedModel 

pub enum FittedModel {
    Standard {
        payload: FittedModelPayload,
    },
    LocationScale {
        payload: FittedModelPayload,
    },
    MarginalSlope {
        payload: FittedModelPayload,
    },
    Survival {
        payload: FittedModelPayload,
    },
    TransformationNormal {
        payload: FittedModelPayload,
    },
}

Variants

Standard

Fields

payload: FittedModelPayload

LocationScale

Fields

payload: FittedModelPayload

MarginalSlope

Fields

payload: FittedModelPayload

Survival

Fields

payload: FittedModelPayload

TransformationNormal

Fields

payload: FittedModelPayload

Implementations

impl FittedModel

pub fn axis_clip_to_training_ranges( &self, data: ArrayView2<'_, f64>, col_map: &HashMap<String, usize>, ) -> Option<Array2<f64>>

Axis-clip each continuous new-data column to the (min, max) range observed in training. Categorical and binary columns are left untouched so unseen levels surface rather than being silently remapped onto seen ones. Returns Some(clipped_copy) only if at least one value was actually clipped; otherwise None so callers can avoid owning a redundant copy. Pre-2026-04-29 model JSONs that lack the training_feature_ranges field deserialize to None and pass through unchanged.

pub fn from_payload(payload: FittedModelPayload) -> Self

pub fn payload(&self) -> &FittedModelPayload

pub fn likelihood(&self) -> LikelihoodSpec

pub fn prediction_required_columns(&self) -> Result<BTreeSet<String>, String>

Columns this model consumes from a prediction frame — its input contract.

Every variable named by the main formula (features, interaction margins, random-effect groups, and a smooth’s by= column), the survival entry/exit columns or the transformation-normal response, the auxiliary noise / logslope formula columns, and the offset / noise-offset / latent-z columns. The event-indicator and the plain response of a standard model are deliberately excluded: they are not needed to form a prediction (the conformal-calibration fold layers the response back on separately).

This is the single authority shared by the CLI and PyFFI predict paths. A prediction frame column that is not in this set is irrelevant to the model and must be ignored rather than strict-encoded against the training schema — otherwise an unrelated ID/label column with a held-out categorical level aborts predict (#840).

pub fn diagnostic_extra_columns(&self) -> Result<Vec<String>, String>

Columns a post-fit diagnostic command (diagnose / sample / report) needs beyond Self::prediction_required_columns.

Prediction deliberately drops a standard GAM’s bare response so a prediction frame may omit it (#840 / #864). Diagnostics are statements about that observed response — residuals, R², posterior likelihoods, leave-one-out — so the response must be present. This returns the bare response column when the prediction projection would otherwise drop it, and nothing when the response is already prediction-required (survival Surv(...) time/event columns, the transformation-normal response) or is not a plain data column.

Centralising the intent here is what makes it structurally impossible for a diagnostic command to silently drop the response: callers use load_dataset…_for_diagnostics, which always folds these in, instead of each remembering to thread an extra_required response by hand.

pub fn predict_model_class(&self) -> PredictModelClass

pub fn saved_link_wiggle( &self, ) -> Result<Option<SavedLinkWiggleRuntime>, FittedModelError>

pub fn saved_baseline_time_wiggle( &self, ) -> Result<Option<SavedBaselineTimeWiggleRuntime>, FittedModelError>

pub fn has_link_wiggle(&self) -> bool

Whether this model has a link wiggle component with complete metadata.

pub fn has_baseline_time_wiggle(&self) -> bool

Whether this model has a baseline-time wiggle component with complete metadata.

pub fn prediction_uses_posterior_mean(&self) -> bool

Whether the default point prediction must integrate the inverse link over the coefficient posterior — reporting the posterior mean E[g⁻¹(Xβ)] — rather than plugging in the posterior mode g⁻¹(Xβ̂).

SPEC (issue #960): the posterior mean is always the default point estimate (never MAP). It is observably distinct from the plug-in exactly when the inverse link is curved over the posterior’s uncertainty, so E[g⁻¹(η)] ≠ g⁻¹(E[η]) by Jensen. The curvature-based classification is:

• all log-link families (Poisson / Gamma / Tweedie / NegativeBinomial): E[exp η] = exp(η + se²/2) ≠ exp(η) (log-normal MGF);
• all Binomial links (logit / probit / cloglog / SAS / BetaLogistic / Mixture / LatentCLogLog): bounded sigmoidal inverse links;
• Beta (logit link): E[σ(η)] ≠ σ(E[η]);
• Royston–Parmar (curved survival-probability inverse link). The integral collapses to the plug-in (so the cheaper plug-in path is exact and taken instead) only for the effectively-linear identity-link Gaussian. Any model carrying a link wiggle or baseline-time wiggle is curved regardless of family. This curvature partition mirrors families::family_runtime::posterior_mean, the compute path that produces the corrected mean for each of these families.

This is the single source of truth shared by the CLI (gam predict) and the Python FFI prediction path so the two can never drift on which models receive the posterior-mean correction.

pub fn saved_prediction_runtime( &self, ) -> Result<SavedPredictionRuntime, FittedModelError>

pub fn saved_sas_state(&self) -> Result<Option<SasLinkState>, FittedModelError>

pub fn saved_beta_logistic_state( &self, ) -> Result<Option<SasLinkState>, FittedModelError>

pub fn saved_mixture_state( &self, ) -> Result<Option<MixtureLinkState>, FittedModelError>

pub fn saved_latent_cloglog_state( &self, ) -> Result<Option<LatentCLogLogState>, FittedModelError>

pub fn resolved_inverse_link( &self, ) -> Result<Option<InverseLink>, FittedModelError>

pub fn measure_jet_extrapolation_variance( &self, data: ArrayView2<'_, f64>, col_map: &HashMap<String, usize>, ) -> Result<Option<Array1<f64>>, FittedModelError>

V∞ §5 producer: per-row measure-jet extrapolation variance on the η scale for a prediction batch (docs/measure_jet_v_infinity.md).

For every frozen measure-jet term in resolved_termspec this prices the off-support ignorance of the fitted multiscale spectrum at each query row: support curve from the frozen nodes/masses/band (gam_terms::basis::measure_jet_support_curve), fitted per-scale amplitudes λ̂_ℓ read from the fit’s lambdas through the replayed design’s penalty layout, folded through gam_terms::basis::measure_jet_extrapolation_variance and scaled by the fit’s coefficient-covariance scale φ̂ so the result sits on Vp’s η-variance scale. Terms not yet frozen (no frozen_quadrature or non-UserProvided centers) are skipped with a warning. Returns Ok(None) when no measure-jet term contributes, so callers leave PredictUncertaintyOptions::extrapolation_variance untouched.

data must be the RAW (unclipped) prediction matrix in prediction column order — clipping to the training ranges would freeze the distance signal at the hull and defeat the honesty contract — and col_map the prediction header → column map (the same map handed to the design builder). This is the minimal-plumbing producer seam: the option-building callers (CLI predict, FFI) hold exactly (model, data, col_map) at the point where they assemble PredictUncertaintyOptions, and the fusion in predict_gamwith_uncertainty adds the array AFTER its multiplicative inflations: Var_total = Var_Vp·inflation + Var_extrap.

pub fn unified(&self) -> Option<&UnifiedFitResult>

Access the unified fit result, if stored.

pub fn load_from_path(path: &Path) -> Result<Self, FittedModelError>

pub fn save_to_path(&self, path: &Path) -> Result<(), FittedModelError>

pub fn require_data_schema(&self) -> Result<&DataSchema, FittedModelError>

pub fn saved_spline_scan( &self, ) -> Result<Option<(&str, SplineScanFit)>, FittedModelError>

Restore the exact in-memory spline-scan fit from a scan-bearing payload (#1030/#1034). Ok(None) for dense models; the returned predict replays the training Gaussian bridge bit-for-bit.

pub fn saved_residual_cascade( &self, ) -> Result<Option<(&[String], ResidualCascadeFit)>, FittedModelError>

Restore the in-memory residual-cascade fit from a cascade-bearing payload (#1032). Ok(None) for non-cascade models; the returned fit replays the multilevel Wendland-frame posterior for the d ∈ {2, 3} feature columns at each predict point.

pub fn random_effect_group_columns(&self) -> HashSet<String>

pub fn validate_for_persistence(&self) -> Result<(), FittedModelError>

pub fn validate_numeric_finiteness(&self) -> Result<(), FittedModelError>

Methods from Deref<Target = FittedModelPayload>

pub fn set_training_feature_metadata( &mut self, headers: Vec<String>, feature_ranges: Vec<(f64, f64)>, )

pub fn apply_survival_time_basis(&mut self, snapshot: &SavedSurvivalTimeBasis)

Write the persistable time-basis snapshot for a survival model.

This is the only path that should populate the survival_time_* fields used by the loader. Routing every FFI builder through this helper guarantees no builder can silently drop a field — the marginal-slope save→load bug was a builder that missed survival_time_basis.

Trait Implementations

impl Clone for FittedModel

fn clone(&self) -> FittedModel

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Deref for FittedModel

type Target = FittedModelPayload

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl DerefMut for FittedModel

fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.

impl<'de> Deserialize<'de> for FittedModel

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for FittedModel

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.