Struct FitConfig 

pub struct FitConfig {

    pub family: Option<String>,
    pub negative_binomial_theta: Option<f64>,
    pub link: Option<String>,
    pub flexible_link: bool,
    pub offset_column: Option<String>,
    pub noise_offset_column: Option<String>,
    pub frailty: Option<FrailtySpec>,
    pub baseline_target: String,
    pub baseline_scale: Option<f64>,
    pub baseline_shape: Option<f64>,
    pub baseline_rate: Option<f64>,
    pub baseline_makeham: Option<f64>,
    pub time_basis: String,
    pub time_degree: usize,
    pub time_num_internal_knots: usize,
    pub time_smooth_lambda: f64,
    pub survival_likelihood: String,
    pub survival_distribution: String,
    pub threshold_time_k: Option<usize>,
    pub threshold_time_degree: usize,
    pub sigma_time_k: Option<usize>,
    pub sigma_time_degree: usize,
    pub noise_formula: Option<String>,
    pub logslope_formula: Option<String>,
    pub z_column: Option<String>,
    pub weight_column: Option<String>,
    pub expectile_tau: Option<f64>,
    pub ctn_stage1: Option<CtnStage1Recipe>,
    pub scale_dimensions: bool,
    pub adaptive_regularization: Option<bool>,
    pub ridge_lambda: f64,
    pub transformation_normal: bool,
    pub firth: bool,
    pub outer_max_iter: Option<usize>,
    pub outer_wall_clock_budget_secs: Option<f64>,
    pub gpu_policy: GpuPolicy,
    pub resource_policy: Option<ResourcePolicy>,
    pub group_metadata: Option<BTreeMap<String, Value>>,
    pub coefficient_groups: Vec<CoefficientGroupSpec>,
    pub penalty_block_gamma_priors: Vec<(String, f64, f64)>,
    pub latents: Option<Value>,
    pub analytic_penalties: Option<Value>,
    pub topology_auto_selector: Option<TopologyAutoSelector>,
    pub smooth_overrides: Option<Value>,
    pub persist_warm_start_disk: bool,
}

Fields

family: Option<String>

Family: “gaussian”, “binomial”, “poisson”, “negative-binomial”, “gamma”, “tweedie” (alias “tw”; variance power fixed at p = 1.5), or None for auto-detect.

negative_binomial_theta: Option<f64>

Fixed size/overdispersion parameter for family="negative-binomial".

link: Option<String>

Link: “identity”, “logit”, “probit”, “cloglog”, “sas”, “beta-logistic”, or None.

flexible_link: bool

Whether to use flexible (wiggle-augmented) link.

offset_column: Option<String>

Optional additive offset column for the primary linear predictor.

noise_offset_column: Option<String>

Optional additive offset column for the noise/log-scale predictor.

frailty: Option<FrailtySpec>

Optional family-level frailty modifier.

baseline_target: String

Baseline target: “linear”, “weibull”, “gompertz”, “gompertz-makeham”.

baseline_scale: Option<f64>

baseline_shape: Option<f64>

baseline_rate: Option<f64>

baseline_makeham: Option<f64>

time_basis: String

Time basis: “ispline” or “none”.

time_degree: usize

time_num_internal_knots: usize

time_smooth_lambda: f64

survival_likelihood: String

Survival likelihood mode: “location-scale”, “transformation”, “weibull”, “marginal-slope”, “latent”, or “latent-binary”.

survival_distribution: String

Residual distribution: “gaussian”, “logistic”, “gumbel”.

threshold_time_k: Option<usize>

threshold_time_degree: usize

sigma_time_k: Option<usize>

sigma_time_degree: usize

noise_formula: Option<String>

If set, fit a location-scale model with this formula for the noise parameter.

logslope_formula: Option<String>

Formula for the log-slope model (survival marginal-slope or Bernoulli marginal-slope).

z_column: Option<String>

Column name for the z (exposure/dose) variable in marginal-slope models.

weight_column: Option<String>

Optional non-negative per-row training weights column.

expectile_tau: Option<f64>

Expectile asymmetry τ ∈ (0, 1) for family = "expectile".

When family resolves to "expectile" the fit minimizes the Newey–Powell asymmetric squared loss Σ wᵢ(τ)·(yᵢ − μᵢ)² with wᵢ(τ) = τ if yᵢ > μᵢ else 1 − τ, tracing the conditional τ-expectile — the smooth analogue of the τ-quantile. τ = 0.5 reduces exactly to the Gaussian-identity mean fit. The whole penalized smooth + REML λ-selection machinery is reused via a Least Asymmetrically Weighted Squares (LAWS) outer loop. None defaults to the median expectile τ = 0.5 when the family is "expectile"; it is ignored for every other family. The asymmetry may also be written inline as family = "expectile(0.9)", which fills this field at resolve time.

ctn_stage1: Option<CtnStage1Recipe>

Internal CTN Stage-1 provenance for the marginal-slope z column.

When the marginal-slope z was generated by a transformation-normal Stage-1 fit, the orchestration layer fills this with the Stage-1 recipe. Its presence is the sole auto-enable signal for cross-fitted, Neyman- orthogonal score calibration (#461): the materializer cross-fits the CTN to produce out-of-fold z and the score-influence Jacobian J, replaces the raw z with z_oof, and absorbs J as a leakage-projection block in Stage-2. This is in-process plumbing only — there is no CLI flag, env var, or feature gate. None ⇒ raw z with the free-warp score_warp fallback. See CtnStage1Recipe.

scale_dimensions: bool

adaptive_regularization: Option<bool>

Enable exact spatial adaptive regularization for standard formula fits. None uses the quality-first automatic policy. The current automatic policy leaves LAREG off unless explicitly requested because the optimizer’s REML-selected local weights can over-regularize small high-yield spatial signals.

ridge_lambda: f64

transformation_normal: bool

Route the fit through the transformation-normal family. When set, the formula terms are treated as the covariate side of the transformation model and the response basis is built internally. Incompatible with noise_formula and with Surv(...) responses.

firth: bool

Enable Firth bias reduction for standard single-parameter families.

outer_max_iter: Option<usize>

Optional cap on the REML/LAML outer smoothing-parameter iterations for standard formula fits. None uses the production default.

outer_wall_clock_budget_secs: Option<f64>

Optional wall-clock budget (seconds) for the outer smoothing search (gam#979). Threaded to the survival marginal-slope fit, whose constrained joint-Newton can fail to certify convergence and otherwise grind without bound; with this set the fit returns its best-so-far iterate (or a catchable error) within the budget instead of hanging. None keeps the generous built-in default for that path and is unbounded elsewhere.

gpu_policy: GpuPolicy

GPU backend selection policy. Auto uses supported device kernels for large workloads, Off pins execution to CPU kernels, and Force fails loudly when a requested GPU kernel has no compiled backend.

resource_policy: Option<ResourcePolicy>

Optional override of the gam_runtime::resource::ResourcePolicy used when planning spatial bases (TPS / Matern / Duchon) during term construction. When None, the default-library policy is used.

group_metadata: Option<BTreeMap<String, Value>>

Optional per-group metadata supplied by the caller. Fitting ignores this field; saved-model builders pass it through so deployment consumers can recover group provenance.

coefficient_groups: Vec<CoefficientGroupSpec>

Optional user-defined coefficient groups with separate precision parameters. Group-local priors, including catalog-metadata-informed Gamma precision hyperpriors, are resolved during design setup.

penalty_block_gamma_priors: Vec<(String, f64, f64)>

Optional per-existing-penalty-block Gamma(shape, rate) precision hyperpriors keyed by penalty-block label. This is the catalog-metadata-informed-prior hook for models that do not need a new user-defined coefficient group.

latents: Option<Value>

Python gamfit.fit(..., latents={...}) configuration. This reaches the standard formula workflow as an owned latent-coordinate block: the named smooth’s synthetic covariates are rebuilt from t, and joint REML optimizes [rho, vec(t)] through latent design hyper-dirs.

analytic_penalties: Option<Value>

Python gamfit.fit(..., penalties=[...]) analytic-penalty descriptors, validated against the declared latent-coordinate blocks before a standard latent fit starts.

topology_auto_selector: Option<TopologyAutoSelector>

Formula-path latent topology selector descriptor. The selector itself fits candidates through the ordinary workflow; this slot lets callers request and validate that path from the same config registry.

smooth_overrides: Option<Value>

gamfit.fit(..., smooths={...}) Python kwarg routed through the FFI bridge. JSON object keyed by formula symbol (single column name or comma-joined tuple) → smooth descriptor ({"kind": "duchon", "centers": [[...], ...], ...}). Applied as a post-processing step on the TermCollectionSpec produced by the formula DSL: each smooth term whose feature_cols match a registry key has its kind-specific tunables (centers, knots, kernel hyperparameters) overridden with the user-supplied values. The single canonical lowering path guarantees smooths={"x": Duchon(centers=K)} (integer) produces a bit-identical block spec to writing duchon(x, centers=K) in the formula; only explicit array-valued centers= differs, routing through CenterStrategy::UserProvided instead of FarthestPoint/EqualMass.

persist_warm_start_disk: bool

Engage the cross-process ON-DISK persistent warm-start layer (#1082).

Default false: only the always-on in-memory warm start runs, so a single fit and throwaway/replicate/CI-coverage loops pay zero disk I/O (no WarmStartStore dir/eviction scan, no record load/store). Set true to engage cross-process / repeat-fit resume: the flag threads FitConfig → FitOptions → ExternalOptimOptions down to the standard RemlState, which then calls enable_persistent_warm_start_disk().

Trait Implementations

impl Clone for FitConfig

fn clone(&self) -> FitConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for FitConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for FitConfig

fn default() -> Self

Returns the “default value” for a type.