Struct AloInput 

pub struct AloInput<'a> {
    pub design: &'a Array2<f64>,
    pub penalized_hessian: &'a Array2<f64>,
    pub hessian_weights: SignedWeightsView<'a>,
    pub score_weights: PsdWeightsView<'a>,
    pub working_response: &'a Array1<f64>,
    pub eta: &'a Array1<f64>,
    pub offset: &'a Array1<f64>,
    pub link: LinkFunction,
    pub phi: f64,
    pub penalty_root: Option<&'a Array2<f64>>,
    pub ridge: f64,
    pub score_curvature: Option<&'a AloScalarScoreCurvature<'a>>,
}

Model-agnostic input for ALO diagnostics.

Any model with a design matrix, penalized Hessian, and IRLS geometry can compute ALO leverages and leave-one-out predictions. This decouples ALO from the single-block PIRLS solver and enables diagnostics for GAMLSS, survival, and joint models.

Fields

design: &'a Array2<f64>

Dense design matrix X (n × p).

penalized_hessian: &'a Array2<f64>

Penalized Hessian H = X’WX + S(λ) at convergence (p × p).

hessian_weights: SignedWeightsView<'a>

Hessian-side IRLS weights W_H at convergence (n). Sign-honest: for non-canonical links the observed-information diagonal can have negative entries, so the typed SignedWeightsView is the contract here. PSD callers needing to promote (e.g. the canonical-link case where the caller has discharged W_H ≥ 0 algebraically) can route through SignedWeightsView::as_psd() at the consumer.

score_weights: PsdWeightsView<'a>

Score-side IRLS weights W_S paired with working_response (n). PSD-by-construction: the score-side Fisher weights h'²/(φ V(μ)) ≥ 0.

working_response: &'a Array1<f64>

IRLS working response at convergence (n).

eta: &'a Array1<f64>

Fitted linear predictor η̂ (n).

offset: &'a Array1<f64>

Offset vector (n). Pass zeros if no offset.

link: LinkFunction

Link function (for phi determination).

phi: f64

Dispersion parameter φ. For non-Gaussian families this is 1.0.

penalty_root: Option<&'a Array2<f64>>

Optional penalty square root E with E^T E = S(λ) (rank × p) for sandwich SE. When None, sandwich SE is set equal to Bayesian SE.

ridge: f64

Ridge added to the Hessian for logdet surface.

score_curvature: Option<&'a AloScalarScoreCurvature<'a>>

Optional per-row score/curvature evaluator (i, η) → (ℓ_i'(η), ℓ_i''(η)).

When supplied, the leave-i-out predictor is obtained by solving the frozen-curvature scalar fixed point η = η̂_i + a_ii ℓ_i'(η) to convergence (see [alo_eta_exact_frozen_curvature]) instead of taking a single Newton step. This eliminates the first-order linearization error that the one-step ALO incurs on small-n, strongly curved likelihoods (e.g. binomial logistic regression). Non-convergence or invalid scalar Newton geometry is returned as an ALO error. When None, the classical single-Newton-step ALO formula is used. The evaluator must be consistent with hessian_weights at convergence: ℓ_i''(η̂_i) = W_H[i] and ℓ_i'(η̂_i) = W_S[i]·((η̂_i−o_i) − (z_i−o_i)).

Implementations

impl<'a> AloInput<'a>

pub fn from_geometry( geom: &'a FitGeometry, design: &'a Array2<f64>, eta: &'a Array1<f64>, offset: &'a Array1<f64>, link: LinkFunction, phi: f64, ) -> Self

Build an AloInput from FitGeometry and associated vectors.