Struct AloInput 

pub struct AloInput<'a> {
    pub design: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>,
    pub penalized_hessian: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>,
    pub hessian_weights: SignedWeightsView<'a>,
    pub score_weights: PsdWeightsView<'a>,
    pub working_response: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub eta: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub offset: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>,
    pub phi: f64,
    pub score_curvature: Option<&'a (dyn Fn(usize, f64) -> Result<(f64, f64), AloError> + Sync)>,
}

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 ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>

Dense design matrix X (n × p).

penalized_hessian: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>

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 ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

IRLS working response at convergence (n).

eta: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

Fitted linear predictor η̂ (n).

offset: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

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

phi: f64

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

score_curvature: Option<&'a (dyn Fn(usize, f64) -> Result<(f64, f64), AloError> + Sync)>

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_penalized_hessian_with_working_state( penalized_hessian: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, design: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, eta: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, offset: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, phi: f64, working_weights: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, working_response: &'a ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, ) -> AloInput<'a>

Build an AloInput from an exact saved penalized Hessian plus externally supplied working weights / working response.

The row-sized IRLS working vectors are derived quantities: at convergence they are deterministic functions of the linear predictor η̂ = Xβ̂, the response y, and the family (w_i = h'(η̂_i)²/(φ V(μ̂_i))· prior_i, z_i = η̂_i + (y_i−μ̂_i)/h'(η̂_i)). A saved-model consumer reconstructs them from the saved β by replaying the same PIRLS working-state update the fit used, then feeds them here. The precision comes from the canonical fit’s exact unscaled Hessian accessor; callers do not need a second FitGeometry wrapper or a covariance inversion.

Same canonical (Fisher == Observed) contract as AloInput::from_active_geometry: the supplied working_weights are the score-side Fisher weights and are re-viewed for the Hessian-side slot via as_signed().