gam_solve/inference/

alo.rs

use crate::estimate::EstimationError;
use crate::estimate::{FitGeometry, UnifiedFitResult};
use crate::pirls;
use faer::Mat as FaerMat;
use faer::linalg::matmul::matmul;
use faer::prelude::ReborrowMut;
use faer::{Accum, Par};
use gam_linalg::faer_ndarray::{FaerArrayView, FaerCholesky};
use gam_linalg::matrix::{PsdWeightsView, SignedWeightsView};
use gam_linalg::utils::StableSolver;
use gam_problem::LinkFunction;
use ndarray::{Array1, Array2, ArrayView1, ShapeBuilder, s};
use std::fmt;

/// Typed error variants for the ALO (approximate leave-one-out) diagnostics
/// module.
///
/// Public entry points continue to return `Result<_, EstimationError>`; this
/// enum is materialized at leaf sites and converted at the boundary via
/// `From<AloError> for EstimationError` so error text remains byte-identical
/// to the previous `EstimationError::InvalidInput(format!(...))` /
/// `ModelIsIllConditioned { ... }` output.
#[derive(Debug, Clone)]
pub enum AloError {
    /// Caller-supplied configuration is structurally invalid: dimension
    /// mismatch, non-finite inputs that are not weights/response, missing
    /// PIRLS / geometry artifacts, or out-of-range scalar parameters.
    InvalidInput { reason: String },
    /// IRLS weights or working response contain a non-finite entry, or the
    /// working response itself is invalid.
    WeightInvalid { reason: String },
    /// The dense design matrix required for ALO could not be materialized
    /// from the underlying PIRLS artifact (e.g. sparse-only export).
    DesignDegenerate { reason: String },
    /// The penalized Hessian factorization failed, or downstream diagnostics
    /// produced NaN values that indicate the influence matrix is unusable.
    InfluenceMatrixFailed { condition_number: f64 },
    /// Per-observation ALO computation produced a non-finite value (variance,
    /// denominator, or corrected η̃) at convergence.
    LooComputationFailed { reason: String },
}

impl fmt::Display for AloError {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            AloError::InvalidInput { reason }
            | AloError::WeightInvalid { reason }
            | AloError::DesignDegenerate { reason }
            | AloError::LooComputationFailed { reason } => f.write_str(reason),
            AloError::InfluenceMatrixFailed { condition_number } => {
                write!(
                    f,
                    "ALO influence matrix failed (condition number {condition_number:.3e})"
                )
            }
        }
    }
}

impl std::error::Error for AloError {}

impl From<AloError> for EstimationError {
    fn from(err: AloError) -> EstimationError {
        match err {
            AloError::InvalidInput { reason }
            | AloError::WeightInvalid { reason }
            | AloError::DesignDegenerate { reason }
            | AloError::LooComputationFailed { reason } => EstimationError::InvalidInput(reason),
            AloError::InfluenceMatrixFailed { condition_number } => {
                EstimationError::ModelIsIllConditioned { condition_number }
            }
        }
    }
}

impl From<AloError> for String {
    fn from(err: AloError) -> String {
        err.to_string()
    }
}

/// Approximate leave-one-out diagnostics derived from a fitted model.
#[derive(Debug, Clone)]
pub struct AloDiagnostics {
    pub eta_tilde: Array1<f64>,
    /// Bayesian/conditional standard error on eta:
    /// sqrt(phi * x_i^T H^{-1} x_i).
    pub se_bayes: Array1<f64>,
    /// Frequentist sandwich-style standard error on eta:
    /// sqrt(phi * x_i^T H^{-1} X^T W X H^{-1} x_i).
    pub se_sandwich: Array1<f64>,
    pub pred_identity: Array1<f64>,
    pub leverage: Array1<f64>,
    pub fisherweights: Array1<f64>,
}

#[inline]
fn alo_eta_updatewith_offset(
    eta_hat: f64,
    z: f64,
    offset: f64,
    x_hinv_x: f64,
    score_weight: f64,
    denom: f64,
) -> f64 {
    // PIRLS working-response algebra is centered on offset, so the scalar
    // score uses (eta - offset) - (z - offset).
    let eta_centered = eta_hat - offset;
    let z_centered = z - offset;
    let score = score_weight * (eta_centered - z_centered);
    offset + eta_centered + x_hinv_x * score / denom
}

/// Per-row score and curvature of the penalized NLL contribution as functions
/// of the row's linear predictor `eta`.
///
/// Returns `(ℓ_i'(eta), ℓ_i''(eta))` where `ℓ_i` is the (dispersion-scaled)
/// negative log-likelihood of observation `i` viewed as a univariate function
/// of `eta_i = x_i^T β`. This is the local family geometry that the ALO
/// frozen-curvature fixed point [`alo_eta_exact_frozen_curvature`] iterates to
/// convergence; supplying it upgrades the single-Newton-step ALO correction to
/// the exact leave-`i`-out predictor under a frozen penalized Hessian.
pub type AloScalarScoreCurvature<'a> = dyn Fn(usize, f64) -> (f64, f64) + Sync + 'a;

/// Maximum scalar Newton iterations for the exact frozen-curvature ALO fixed
/// point. The map `r(η) = η − η̂ − a_ii ℓ_i'(η)` is one-dimensional and
/// strongly contractive for the well-leveraged majority of points, so this
/// caps the rare high-leverage / near-separation rows where convergence is
/// slow without ever exceeding O(1) work per observation.
const ALO_EXACT_SCALAR_MAX_ITERS: usize = 64;

/// Absolute convergence tolerance on the scalar residual `r(η)` for the exact
/// frozen-curvature ALO fixed point. Well below the `1e-2` predictive bar the
/// LOO comparison asserts, so the refinement is not the limiting error term.
const ALO_EXACT_SCALAR_TOL: f64 = 1e-12;

/// Solve the frozen-curvature ALO leave-`i`-out fixed point exactly.
///
/// The leave-`i`-out optimum differs from the full fit only through the removed
/// observation, whose gradient/Hessian depend on `β` solely via the scalar
/// `η_i = x_i^T β`. Freezing the penalized Hessian `H` at its converged value
/// reduces the exact leave-`i`-out condition to the scalar equation
///
///   η = η̂_i + a_ii · ℓ_i'(η),     a_ii = x_i^T H^{-1} x_i,
///
/// where `ℓ_i'(η)` is the row's NLL score (so that `∇F = ℓ_i'(η_i) x_i` at the
/// leave-`i`-out point). The single-Newton-step ALO is exactly the first
/// iterate of Newton's method on `r(η) = η − η̂_i − a_ii ℓ_i'(η)` started at
/// `η̂_i`; iterating to convergence captures the change in the held-out point's
/// likelihood curvature (the dominant first-order error on small-`n`, curved
/// likelihoods such as binomial logistic regression near separation).
///
/// `score_curvature(eta)` returns `(ℓ_i'(eta), ℓ_i''(eta))`. The returned value
/// is the corrected linear predictor `η̃_i`. Failure to reach the residual
/// tolerance is reported to the caller; no one-step approximation is substituted
/// for a failed exact solve.
#[derive(Debug, Clone, Copy, PartialEq)]
enum AloExactScalarError {
    NonFiniteScoreCurvature {
        eta: f64,
        ell_prime: f64,
        ell_double: f64,
    },
    DegenerateJacobian {
        eta: f64,
        jacobian: f64,
    },
    NonFiniteStep {
        eta: f64,
        residual: f64,
        jacobian: f64,
        next: f64,
    },
    MaxIterations {
        iterations: usize,
        residual: f64,
        eta: f64,
    },
}

impl fmt::Display for AloExactScalarError {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match *self {
            AloExactScalarError::NonFiniteScoreCurvature {
                eta,
                ell_prime,
                ell_double,
            } => write!(
                f,
                "non-finite score/curvature at eta={eta:.6e}: ell_prime={ell_prime:.6e}, ell_double={ell_double:.6e}"
            ),
            AloExactScalarError::DegenerateJacobian { eta, jacobian } => write!(
                f,
                "degenerate Newton Jacobian at eta={eta:.6e}: jacobian={jacobian:.6e}, min={ALO_DENOMINATOR_MIN:.1e}"
            ),
            AloExactScalarError::NonFiniteStep {
                eta,
                residual,
                jacobian,
                next,
            } => write!(
                f,
                "non-finite Newton step from eta={eta:.6e}: residual={residual:.6e}, jacobian={jacobian:.6e}, next={next:.6e}"
            ),
            AloExactScalarError::MaxIterations {
                iterations,
                residual,
                eta,
            } => write!(
                f,
                "did not converge within {iterations} iterations: residual={residual:.6e}, eta={eta:.6e}, tol={ALO_EXACT_SCALAR_TOL:.1e}"
            ),
        }
    }
}

/// Maximum number of step halvings in the backtracking line search that
/// globalizes the scalar Newton iteration. `2^{-40}` shrinks a unit step well
/// below `ALO_EXACT_SCALAR_TOL` relative to any η of practical magnitude, so a
/// row that cannot make progress within this budget is genuinely stalled rather
/// than merely under-damped.
const ALO_EXACT_SCALAR_BACKTRACKS: usize = 40;

#[inline]
fn alo_eta_exact_frozen_curvature(
    eta_hat: f64,
    a_ii: f64,
    score_curvature: &dyn Fn(f64) -> (f64, f64),
) -> Result<f64, AloExactScalarError> {
    // Residual of the leave-i-out fixed point η = η̂ + a_ii ℓ'(η):
    //   r(η) = η − η̂ − a_ii ℓ'(η),     r'(η) = 1 − a_ii ℓ''(η) = jac.
    // For an exponential-family NLL score ℓ'(η) = c_i(μ(η) − y) on a non-linear
    // (e.g. log) link the curvature ℓ''(η) = c_i μ'(η) grows without bound, so
    // r(η) is concave with an interior maximum where the weighted leverage
    // a_ii ℓ'' passes 1 (jac = 0): the leave-i-out root that limits to η̂ as
    // a_ii → 0 sits on the jac > 0 branch anchored at η̂, while beyond the
    // maximum r turns over and diverges as μ(η) explodes.
    //
    // Two safeguards make the scalar solve globally convergent to that root:
    //
    //   1. Anchor the iteration at η̂ itself, not at the classical one-step ALO
    //      predictor. At η̂ the weighted leverage a_ii ℓ''(η̂) < 1, so jac ≈ 1
    //      and we start strictly inside the correct basin; the brute-force
    //      n-fold reference solves the identical fixed point anchored at η̂.
    //      Seeding at the one-step predictor instead can land a high-leverage
    //      row *past* the interior maximum on the runaway branch, from which no
    //      Newton iteration returns (Poisson/log row 198: η ≈ 6.3, r ≈ −577).
    //
    //   2. Backtrack on the merit ½r(η)². The Newton direction d = −r/jac
    //      satisfies (½r²)'·d = r·jac·(−r/jac) = −r² < 0 for any finite nonzero
    //      jac, so halving the step until |r| strictly decreases never leaves
    //      the basin even if a full step would overshoot the maximum.
    let residual_and_jac = |eta: f64| -> Result<(f64, f64), AloExactScalarError> {
        let (ell_prime, ell_double) = score_curvature(eta);
        if !ell_prime.is_finite() || !ell_double.is_finite() {
            return Err(AloExactScalarError::NonFiniteScoreCurvature {
                eta,
                ell_prime,
                ell_double,
            });
        }
        Ok((eta - eta_hat - a_ii * ell_prime, 1.0 - a_ii * ell_double))
    };

    let mut eta = eta_hat;
    let (mut residual, mut jac) = residual_and_jac(eta)?;
    for _ in 0..ALO_EXACT_SCALAR_MAX_ITERS {
        if residual.abs() <= ALO_EXACT_SCALAR_TOL {
            return Ok(eta);
        }
        if jac.abs() <= ALO_DENOMINATOR_MIN || !jac.is_finite() {
            return Err(AloExactScalarError::DegenerateJacobian { eta, jacobian: jac });
        }
        let step = residual / jac;
        if !step.is_finite() {
            return Err(AloExactScalarError::NonFiniteStep {
                eta,
                residual,
                jacobian: jac,
                next: eta - step,
            });
        }
        // Backtracking line search: take the longest damped Newton step
        // 2^{-k} that strictly reduces the merit |r|. A non-finite trial
        // (score/curvature evaluated in the runaway branch) is treated as no
        // improvement and rejected, so the search retreats toward η̂.
        let mut t = 1.0;
        let mut advanced = false;
        for _ in 0..ALO_EXACT_SCALAR_BACKTRACKS {
            let trial = eta - t * step;
            if let Ok((r_trial, j_trial)) = residual_and_jac(trial) {
                if r_trial.abs() < residual.abs() {
                    eta = trial;
                    residual = r_trial;
                    jac = j_trial;
                    advanced = true;
                    break;
                }
            }
            t *= 0.5;
        }
        if !advanced {
            break;
        }
    }
    Err(AloExactScalarError::MaxIterations {
        iterations: ALO_EXACT_SCALAR_MAX_ITERS,
        residual,
        eta,
    })
}

#[inline]
fn bayesvar_eta(phi: f64, x_hinv_x: f64) -> f64 {
    phi * x_hinv_x
}

#[inline]
fn sandwichvar_eta_from_meat(phi: f64, meat_quad: f64) -> f64 {
    phi * meat_quad
}

#[inline]
fn variance_negative_tolerance(scale: f64) -> f64 {
    // Tight relative tolerance for cancellation from x'H^{-1}x - ||E t||^2 - ridge||t||^2.
    1e-12 * scale.abs().max(1.0)
}

const LEVERAGE_HIGH_THRESHOLD: f64 = 0.99;
const LEVERAGE_VERY_HIGH_THRESHOLD: f64 = 0.999;
const LEVERAGE_RATE_THRESHOLDS: [f64; 3] = [0.90, 0.95, 0.99];
const LEVERAGE_PERCENTILES: [f64; 3] = [0.50, 0.95, 0.99];
const ALO_DENOMINATOR_MIN: f64 = 1e-12;
const MULTIBLOCK_ALO_MEMORY_BUDGET_BYTES: usize = 256 * 1024 * 1024;

/// Number of observation columns solved per blocked right-hand-side batch in the
/// scalar-leverage path. Sizes the reusable `(p, .)` and `(e_rank, .)` scratch
/// buffers so the dense multi-RHS solve stays BLAS-3 (good cache reuse) without
/// materializing all `n` columns at once. The final batch is the remainder.
const ALO_RHS_BLOCK_COLS: usize = 8192;

/// Relative tolerance for accepting the input penalised Hessian `H` as
/// symmetric. We require `|H_ij − H_ji| ≤ HESSIAN_SYMMETRY_REL_TOL ·
/// max(|H_ij|, |H_ji|, 1)`. `1e-8` matches the loosest tolerance any
/// upstream symmetrisation pass leaves on the matrix and is tight enough
/// that a genuinely asymmetric Hessian (a real bug) is caught.
const HESSIAN_SYMMETRY_REL_TOL: f64 = 1e-8;

/// Diagonal ridge added to the local block precision when its LU pivot is
/// below [`LU_PIVOT_SINGULAR_TOL`]. Matches the legacy `eps = 1e-6`
/// regularisation in the prior `det_small < 1e-12` branch — bumping the
/// determinant of `I − W A` (or `I − A W`) safely off zero without
/// perturbing well-conditioned blocks.
const ALO_LOCAL_BLOCK_RIDGE: f64 = 1e-6;

/// Pivot magnitude below which [`lu_factor_in_place`] reports the block
/// `I − W A` as singular and triggers the ridge-regularised refactor.
/// Equivalent to the original `det_small < 1e-12` test on the unfactored
/// determinant.
const LU_PIVOT_SINGULAR_TOL: f64 = 1e-12;

#[inline]
fn percentile_index(sample_size: usize, quantile: f64) -> usize {
    if sample_size <= 1 {
        return 0;
    }
    let max_index = sample_size - 1;
    ((quantile * max_index as f64).round() as usize).min(max_index)
}

#[inline]
fn percentile_from_sorted(sorted: &[f64], quantile: f64) -> f64 {
    if sorted.is_empty() {
        0.0
    } else {
        sorted[percentile_index(sorted.len(), quantile)]
    }
}

#[inline]
fn multiblock_col_offsets(block_designs: &[Array2<f64>]) -> Vec<usize> {
    let mut offsets = Vec::with_capacity(block_designs.len());
    let mut off = 0usize;
    for design in block_designs {
        offsets.push(off);
        off += design.ncols();
    }
    offsets
}

#[inline]
fn multiblock_alo_parallel_leverage_chunk_size(
    p_tot: usize,
    n_blocks: usize,
    n_obs: usize,
    max_workers: usize,
) -> usize {
    if p_tot == 0 || n_blocks == 0 || n_obs == 0 {
        return 1;
    }

    // Each parallel leverage chunk owns q_storage for all block RHS products
    // (B * p_tot * chunk_len) plus one transposed design chunk across all
    // blocks (p_tot * chunk_len).  Divide the global scratch budget by the
    // maximum number of chunks Rayon can execute concurrently so total live
    // per-chunk scratch remains bounded.
    let workers = max_workers.max(1);
    let per_worker_budget = (MULTIBLOCK_ALO_MEMORY_BUDGET_BYTES / workers).max(1);
    let elem_count_per_obs = p_tot.saturating_mul(n_blocks.saturating_add(1)).max(1);
    let bytes_per_obs = elem_count_per_obs
        .saturating_mul(std::mem::size_of::<f64>())
        .max(1);
    let budget_obs = (per_worker_budget / bytes_per_obs).max(1);
    budget_obs.min(n_obs)
}

fn compute_alo_diagnostics_from_pirls_impl(
    base: &pirls::PirlsResult,
    y: ArrayView1<f64>,
    link: LinkFunction,
) -> Result<AloDiagnostics, EstimationError> {
    compute_alo_diagnostics_from_pirls_inner(base, y, link).map_err(EstimationError::from)
}

/// True when the fitted GLM uses a *curved* canonical link, so that the row NLL
/// score and curvature satisfy `ℓ_i'(η) = c_i(μ(η)−y_i)` and `ℓ_i''(η) = c_i μ'(η)`
/// with a single per-row scale `c_i = (prior weight)/φ`. This is the exact
/// condition under which the frozen-curvature ALO scalar fixed point matches
/// the leave-`i`-out refit; only these families enable the exact refinement.
///
/// Gaussian identity is canonical too, but its per-row curvature is *constant*
/// (`μ'(η) ≡ 1`), so the classical Sherman–Morrison one-step ALO is already the
/// exact frozen-Hessian leave-`i`-out solution. Routing it through the scalar
/// Newton closure would only add an O(n) nonlinear solve to diagnostics and
/// quality sweeps without changing the answer, so it is excluded here and falls
/// back to the (exact, for this family) one-step formula.
fn alo_link_needs_exact_curvature_refinement(likelihood: &gam_problem::GlmLikelihoodSpec) -> bool {
    use gam_problem::ResponseFamily;
    matches!(
        (&likelihood.spec.response, likelihood.link_function()),
        (ResponseFamily::Binomial, LinkFunction::Logit)
            | (ResponseFamily::Poisson, LinkFunction::Log)
    )
}

fn compute_alo_diagnostics_from_pirls_inner(
    base: &pirls::PirlsResult,
    y: ArrayView1<f64>,
    link: LinkFunction,
) -> Result<AloDiagnostics, AloError> {
    let x_dense_arc = base
        .x_transformed
        .try_to_dense_arc("ALO diagnostics require dense transformed design")
        .map_err(|reason| AloError::DesignDegenerate { reason })?;
    let x_dense = x_dense_arc.as_ref();
    let n = x_dense.nrows();

    // Compute dispersion parameter.
    let phi = match link {
        LinkFunction::Log => 1.0,
        LinkFunction::Logit
        | LinkFunction::Probit
        | LinkFunction::CLogLog
        | LinkFunction::LogLog
        | LinkFunction::Cauchit
        | LinkFunction::Sas
        | LinkFunction::BetaLogistic => 1.0,
        LinkFunction::Identity => {
            use rayon::iter::{IntoParallelIterator, ParallelIterator};
            let rss: f64 = (0..n)
                .into_par_iter()
                .map(|i| {
                    let r = y[i] - base.finalmu[i];
                    base.finalweights[i] * r * r
                })
                .sum();
            // Effective sample size for dispersion (#584): a zero prior weight
            // makes w_i·r_i² = 0, so the row is already excluded from the RSS
            // numerator and must be excluded from the denominator too. Count only
            // positive-weight rows, exactly as the main optimizer path does
            // (optimizer.rs ~1567); using the raw row count over a zero-excluding
            // numerator biases φ̂ low and shrinks every ALO SE.
            let n_pos = (0..n).filter(|&i| base.finalweights[i] > 0.0).count();
            let dof = (n_pos as f64) - base.edf;
            let denom = dof.max(1.0);
            rss / denom
        }
    };

    let e = &base.reparam_result.e_transformed;
    let ridge = base.ridge_passport.laplacehessianridge().max(0.0);

    // ALO needs the exact penalized Hessian materialized densely for chunked
    // column solves via StableSolver.  The PIRLS export path validates the
    // matrix instead of falling back to a numerical Hessian approximation.
    let h_dense_for_alo = base
        .dense_stabilizedhessian_transformed(
            "ALO diagnostics require exact dense stabilized penalized Hessian",
        )
        .map_err(|e| match e {
            EstimationError::InvalidInput(reason) => AloError::InvalidInput { reason },
            other => AloError::InvalidInput {
                reason: format!("{other:?}"),
            },
        })?;

    // Exact frozen-curvature ALO refinement for canonical-link GLMs.
    //
    // For a canonical link the row NLL score and curvature are
    //   ℓ_i'(η)  = c_i · (μ(η) − y_i),     ℓ_i''(η) = c_i · μ'(η),
    // with c_i = (prior weight)/φ recovered from the converged geometry as
    // c_i = W_H[i] / μ'(η̂_i) (since W_H[i] = c_i μ'(η̂_i) at convergence).
    // Supplying this evaluator lets `compute_alo_from_input_inner` solve the
    // leave-i-out scalar fixed point η = η̂_i + a_ii ℓ_i'(η) exactly instead of
    // taking a single Newton step, removing the first-order linearization error
    // that dominates on small-n, strongly curved likelihoods (binomial logit).
    //
    // Restricted to canonical links because only there does the observed
    // curvature carried by the frozen Hessian (W_H) coincide with c_i μ'(η) for
    // every trial η; non-canonical links retain the classical one-step ALO.
    // Per-row scale c_i = W_H[i]/μ'(η̂_i). Rows whose μ'(η̂_i) is negligible
    // (saturated / near-separation) get c_i = NaN, which makes the exact solver
    // reject that row explicitly rather than substituting the classical one-step
    // ALO.
    let canonical_scale: Option<Array1<f64>> =
        if alo_link_needs_exact_curvature_refinement(&base.likelihood) {
            let mut c = Array1::<f64>::zeros(n);
            for i in 0..n {
                let dmu = base.solve_dmu_deta[i];
                let w_h = base.finalweights[i];
                c[i] = if dmu.abs() <= ALO_DENOMINATOR_MIN || !dmu.is_finite() || !w_h.is_finite() {
                    f64::NAN
                } else {
                    w_h / dmu
                };
            }
            Some(c)
        } else {
            None
        };

    let inv_link_for_closure = base.likelihood.spec.link.clone();
    let score_curvature_closure = canonical_scale.as_ref().map(|scale| {
        move |i: usize, eta: f64| -> (f64, f64) {
            let (mu, dmu) = crate::mixture_link::inverse_link_mu_d1_for_inverse_link(
                &inv_link_for_closure,
                eta,
            )
            .unwrap_or((f64::NAN, f64::NAN));
            let c_i = scale[i];
            (c_i * (mu - y[i]), c_i * dmu)
        }
    });
    let score_curvature_ref: Option<&AloScalarScoreCurvature> = score_curvature_closure
        .as_ref()
        .map(|f| f as &AloScalarScoreCurvature);

    // Build model-agnostic AloInput from PIRLS geometry, then delegate.
    let input = AloInput {
        design: x_dense,
        penalized_hessian: &h_dense_for_alo,
        hessian_weights: base.final_weights_signed(),
        score_weights: base.solve_weights_psd(),
        working_response: &base.solveworking_response,
        eta: &base.final_eta,
        offset: &base.final_offset,
        link,
        phi,
        penalty_root: if e.nrows() > 0 { Some(e) } else { None },
        ridge,
        score_curvature: score_curvature_ref,
    };

    let result = compute_alo_from_input_inner(&input)?;

    // PIRLS-specific post-hoc leverage diagnostics logging.
    log_leverage_diagnostics(&result.leverage, phi);

    // Final NaN guard with detailed error reporting.
    let has_nan_pred = result.eta_tilde.iter().any(|&x| x.is_nan());
    let has_nan_se_bayes = result.se_bayes.iter().any(|&x| x.is_nan());
    let has_nan_se_sandwich = result.se_sandwich.iter().any(|&x| x.is_nan());
    let has_nan_leverage = result.leverage.iter().any(|&x| x.is_nan());

    if has_nan_pred || has_nan_se_bayes || has_nan_se_sandwich || has_nan_leverage {
        log::error!("[GAM ALO] NaN values found in ALO diagnostics:");
        log::error!(
            "[GAM ALO] eta_tilde: {} NaN values",
            result.eta_tilde.iter().filter(|&&x| x.is_nan()).count()
        );
        log::error!(
            "[GAM ALO] se_bayes: {} NaN values",
            result.se_bayes.iter().filter(|&&x| x.is_nan()).count()
        );
        log::error!(
            "[GAM ALO] se_sandwich: {} NaN values",
            result.se_sandwich.iter().filter(|&&x| x.is_nan()).count()
        );
        log::error!(
            "[GAM ALO] leverage: {} NaN values",
            result.leverage.iter().filter(|&&x| x.is_nan()).count()
        );
        return Err(AloError::InfluenceMatrixFailed {
            condition_number: f64::INFINITY,
        });
    }

    Ok(result)
}

/// Log detailed leverage percentile diagnostics for a completed ALO computation.
fn log_leverage_diagnostics(leverage: &Array1<f64>, phi: f64) {
    let n = leverage.len();
    if n == 0 {
        return;
    }

    let mut invalid_count = 0usize;
    let mut high_leverage_count = 0usize;
    let mut threshold_counts = [0usize; LEVERAGE_RATE_THRESHOLDS.len()];
    let mut finite_leverage = Vec::with_capacity(n);

    for (obs, &ai) in leverage.iter().enumerate() {
        if ai.is_finite() {
            finite_leverage.push(ai);
        }

        if !(0.0..=1.0).contains(&ai) || !ai.is_finite() {
            invalid_count += 1;
            log::warn!("[GAM ALO] invalid leverage at i={}, a_ii={:.6e}", obs, ai);
        } else if ai > LEVERAGE_HIGH_THRESHOLD {
            high_leverage_count += 1;
            if ai > LEVERAGE_VERY_HIGH_THRESHOLD {
                log::warn!("[GAM ALO] very high leverage at i={}, a_ii={:.6e}", obs, ai);
            }
        }

        for (idx, threshold) in LEVERAGE_RATE_THRESHOLDS.iter().enumerate() {
            if ai > *threshold {
                threshold_counts[idx] += 1;
            }
        }
    }

    if invalid_count > 0 || high_leverage_count > 0 {
        log::warn!(
            "[GAM ALO] leverage diagnostics: {} invalid values, {} high values (>0.99)",
            invalid_count,
            high_leverage_count
        );
    }

    finite_leverage.sort_by(f64::total_cmp);

    let finite_n = finite_leverage.len();
    let a_mean = if finite_n > 0 {
        finite_leverage.iter().copied().sum::<f64>() / finite_n as f64
    } else {
        0.0
    };
    let a_median = percentile_from_sorted(&finite_leverage, LEVERAGE_PERCENTILES[0]);
    let a_p95 = percentile_from_sorted(&finite_leverage, LEVERAGE_PERCENTILES[1]);
    let a_p99 = percentile_from_sorted(&finite_leverage, LEVERAGE_PERCENTILES[2]);
    let a_max = finite_leverage.last().copied().unwrap_or(0.0);

    // Routine per-ALO leverage summary: a diagnostic snapshot, not an
    // anomaly. Emitted at `info!` so it is visible when the host raises
    // verbosity (CLI `-v`; `gamfit.set_log_level("info")`) but silent at the
    // default `Warn` level (genuine anomalies — invalid / very
    // high leverage — are logged at `warn!` above and stay visible). This
    // line fires once per ALO computation, which recurs across the outer
    // smoothing loop, so at `warn!` it was a dominant source of stderr noise
    // on perfectly healthy fits (#1689).
    log::info!(
        "[GAM ALO] leverage: n={}, mean={:.3e}, median={:.3e}, p95={:.3e}, p99={:.3e}, max={:.3e}",
        n,
        a_mean,
        a_median,
        a_p95,
        a_p99,
        a_max
    );
    log::info!(
        "[GAM ALO] high-leverage: a>0.90: {:.2}%, a>0.95: {:.2}%, a>0.99: {:.2}%, dispersion phi={:.3e}",
        100.0 * (threshold_counts[0] as f64) / n as f64,
        100.0 * (threshold_counts[1] as f64) / n as f64,
        100.0 * (threshold_counts[2] as f64) / n as f64,
        phi
    );
}

/// 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.
pub struct AloInput<'a> {
    /// Dense design matrix X (n × p).
    pub design: &'a Array2<f64>,
    /// Penalized Hessian H = X'WX + S(λ) at convergence (p × p).
    pub penalized_hessian: &'a Array2<f64>,
    /// 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.
    pub hessian_weights: SignedWeightsView<'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`.
    pub score_weights: PsdWeightsView<'a>,
    /// IRLS working response at convergence (n).
    pub working_response: &'a Array1<f64>,
    /// Fitted linear predictor η̂ (n).
    pub eta: &'a Array1<f64>,
    /// Offset vector (n). Pass zeros if no offset.
    pub offset: &'a Array1<f64>,
    /// Link function (for phi determination).
    pub link: LinkFunction,
    /// Dispersion parameter φ. For non-Gaussian families this is 1.0.
    pub phi: 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.
    pub penalty_root: Option<&'a Array2<f64>>,
    /// Ridge added to the Hessian for logdet surface.
    pub ridge: f64,
    /// 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))`.
    pub score_curvature: Option<&'a AloScalarScoreCurvature<'a>>,
}

impl<'a> AloInput<'a> {
    /// Build an `AloInput` from `FitGeometry` and associated vectors.
    pub fn from_geometry(
        geom: &'a FitGeometry,
        design: &'a Array2<f64>,
        eta: &'a Array1<f64>,
        offset: &'a Array1<f64>,
        link: LinkFunction,
        phi: f64,
    ) -> Self {
        // FitGeometry stores one working-weight vector, so this constructor is
        // exact only when the score- and Hessian-side IRLS weights coincide
        // (canonical-link case where Fisher == Observed). In that path the
        // diagonal is the Fisher weight `h'²/(φ V(μ)) ≥ 0`, so the PSD
        // obligation is discharged algebraically without a runtime scan;
        // `as_signed()` re-views the same buffer for the Hessian-side slot.
        let psd_w = PsdWeightsView::from_view_unchecked(geom.working_weights.view());
        Self {
            design,
            penalized_hessian: &geom.penalized_hessian,
            hessian_weights: psd_w.as_signed(),
            score_weights: psd_w,
            working_response: &geom.working_response,
            eta,
            offset,
            link,
            phi,
            penalty_root: None,
            ridge: 0.0,
            score_curvature: None,
        }
    }

    /// Build an `AloInput` from a `FitGeometry`'s 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 size-compacted saved model
    /// keeps the p×p `penalized_hessian` (n-independent) but drops those n-sized
    /// vectors; a post-fit consumer such as `gam diagnose` reconstructs them from
    /// the saved `β` by replaying the same PIRLS working-state update the fit
    /// used, then feeds them here. This preserves the size win of dropping the
    /// working vectors from persistence while still serving the exact geometry
    /// ALO path (no refit, exact saved Hessian).
    ///
    /// Same canonical (Fisher == Observed) contract as [`from_geometry`]: the
    /// supplied `working_weights` are the score-side Fisher weights and are
    /// re-viewed for the Hessian-side slot via `as_signed()`.
    ///
    /// [`from_geometry`]: AloInput::from_geometry
    pub fn from_geometry_with_working_state(
        geom: &'a FitGeometry,
        design: &'a Array2<f64>,
        eta: &'a Array1<f64>,
        offset: &'a Array1<f64>,
        link: LinkFunction,
        phi: f64,
        working_weights: &'a Array1<f64>,
        working_response: &'a Array1<f64>,
    ) -> Self {
        let psd_w = PsdWeightsView::from_view_unchecked(working_weights.view());
        Self {
            design,
            penalized_hessian: &geom.penalized_hessian,
            hessian_weights: psd_w.as_signed(),
            score_weights: psd_w,
            working_response,
            eta,
            offset,
            link,
            phi,
            penalty_root: None,
            ridge: 0.0,
            score_curvature: None,
        }
    }
}

/// Compute ALO diagnostics from model-agnostic inputs.
///
/// This is the generalized entry point that works for any model type.
/// For standard single-block GAMs, prefer `compute_alo_diagnostics_from_fit`
/// which automatically extracts the PIRLS geometry (including sandwich SE).
pub fn compute_alo_from_input(input: &AloInput) -> Result<AloDiagnostics, EstimationError> {
    compute_alo_from_input_inner(input).map_err(EstimationError::from)
}

fn compute_alo_from_input_inner(input: &AloInput) -> Result<AloDiagnostics, AloError> {
    let x_dense = input.design;
    let n = x_dense.nrows();
    let p = x_dense.ncols();
    // Bind the underlying ArrayView1 once so the loop body can index and
    // borrow as before; the sign-character contract lives in the
    // `AloInput` field types, not in this local binding.
    let w_h = input.hessian_weights.view();
    let w_s = input.score_weights.view();

    validate_alo_solve_setup(input, n, p)?;

    let factor = StableSolver::new("alo penalized hessian")
        .factorize(input.penalized_hessian)
        .map_err(|_| AloError::InfluenceMatrixFailed {
            condition_number: f64::INFINITY,
        })?;

    let xt = x_dense.t();
    let phi = input.phi;

    let mut aii = Array1::<f64>::zeros(n);
    let mut x_hinv_x_diag = Array1::<f64>::zeros(n);
    let mut se_bayes = Array1::<f64>::zeros(n);
    let mut se_sandwich = Array1::<f64>::zeros(n);

    let block_cols = ALO_RHS_BLOCK_COLS;
    // Allocate the RHS scratch in column-major (Fortran) order so its column
    // slices are contiguous and align with faer's column-major solve output.
    // This removes redundant `xrow = x_dense.row(obs)` indirection inside the
    // per-observation loop: rhs_chunk_buf already holds X^T at the right cols.
    let mut rhs_chunk_buf = Array2::<f64>::zeros((p, block_cols).f());
    // Reusable faer column-major buffer for X*S, where S = H^{-1}X_i for the
    // current RHS chunk.  The sandwich SE must use the same frozen-curvature
    // meat as the exact LOO reference, `X' W X`, directly; reconstructing it as
    // `H - S_penalty - ridge*I` is brittle because the exported stabilized
    // Hessian may include curvature/stabilization details that are not exactly
    // represented by the penalty root plus public ridge scalar.
    let mut xs_chunk_storage = FaerMat::<f64>::zeros(n, block_cols);
    let x_dense_view = FaerArrayView::new(x_dense);

    for chunk_start in (0..n).step_by(block_cols) {
        let chunk_end = (chunk_start + block_cols).min(n);
        let width = chunk_end - chunk_start;

        rhs_chunk_buf
            .slice_mut(s![.., ..width])
            .assign(&xt.slice(s![.., chunk_start..chunk_end]));

        let rhs_chunkview = rhs_chunk_buf.slice(s![.., ..width]);
        let rhs_chunk = FaerArrayView::new(&rhs_chunkview);
        // s_chunk is owned column-major faer storage; its column slices are
        // contiguous and can be read directly via `col_as_slice` — no need to
        // materialize a parallel ndarray copy.
        let s_chunk = factor.solve(rhs_chunk.as_ref());

        let mut xs_target = xs_chunk_storage.as_mut().subcols_mut(0, width);
        matmul(
            xs_target.rb_mut(),
            Accum::Replace,
            x_dense_view.as_ref(),
            s_chunk.as_ref(),
            1.0,
            Par::Seq,
        );

        let rhs_view = rhs_chunk_buf.slice(s![.., ..width]);

        for local_col in 0..width {
            let obs = chunk_start + local_col;
            // rhs is column-major Fortran ndarray; faer Mat columns are
            // contiguous by construction. Both accesses borrow the existing
            // storage directly — no per-column copy.
            let rhs_col = rhs_view.column(local_col);
            let rhs_slice = rhs_col.as_slice().expect("column-major col contiguous");
            let s_slice = s_chunk.col_as_slice(local_col);

            let mut x_hinv_x = 0.0f64;
            // Fused dot product over the current solve column.
            for k in 0..p {
                let sval = s_slice[k];
                let xval = rhs_slice[k];
                x_hinv_x = sval.mul_add(xval, x_hinv_x);
            }
            let ai = w_h[obs].max(0.0) * x_hinv_x;
            aii[obs] = ai;
            x_hinv_x_diag[obs] = x_hinv_x;

            let var_bayes = bayesvar_eta(phi, x_hinv_x);
            let xs_slice = xs_chunk_storage.col_as_slice(local_col);
            let mut meat_quad = 0.0f64;
            for row in 0..n {
                let xs = xs_slice[row];
                // Sandwich meat is the SCORE covariance Xᵀ diag(W_S) X (Fisher,
                // PSD by construction), not the observed-information Hessian
                // weight W_H: the estimator is Var = H⁻¹·Cov(score)·H⁻¹ with the
                // bread H = Xᵀ W_H X + S. For non-canonical links W_H ≠ W_S (and
                // W_H can be negative), so using W_H here gives a wrong — even
                // negative — sandwich SE. See `AloInput::score_weights`.
                meat_quad += w_s[row] * xs * xs;
            }
            let var_sandwich = sandwichvar_eta_from_meat(phi, meat_quad);

            if !var_bayes.is_finite() || !var_sandwich.is_finite() {
                return Err(AloError::LooComputationFailed {
                    reason: format!(
                        "ALO variance is not finite at row {obs}: bayes={var_bayes:.6e}, sandwich={var_sandwich:.6e}"
                    ),
                });
            }
            let bayes_tol = variance_negative_tolerance(phi * x_hinv_x.abs());
            if var_bayes < -bayes_tol {
                return Err(AloError::LooComputationFailed {
                    reason: format!(
                        "ALO Bayesian variance is materially negative at row {obs}: var={var_bayes:.6e}, tol={bayes_tol:.6e}"
                    ),
                });
            }
            let sandwich_scale = phi * meat_quad.abs().max(x_hinv_x.abs());
            let sandwich_tol = variance_negative_tolerance(sandwich_scale);
            if var_sandwich < -sandwich_tol {
                return Err(AloError::LooComputationFailed {
                    reason: format!(
                        "ALO sandwich variance is materially negative at row {obs}: var={var_sandwich:.6e}, tol={sandwich_tol:.6e}"
                    ),
                });
            }

            se_bayes[obs] = var_bayes.max(0.0).sqrt();
            se_sandwich[obs] = var_sandwich.max(0.0).sqrt();
        }
    }

    let eta_hat = input.eta;
    let z = input.working_response;
    let offset = input.offset;

    use rayon::prelude::*;
    let eta_tilde_vec: Vec<f64> = (0..n)
        .into_par_iter()
        .map(|i| {
            let denom_raw = 1.0 - aii[i];
            if denom_raw <= ALO_DENOMINATOR_MIN || !denom_raw.is_finite() {
                return Err(AloError::LooComputationFailed {
                    reason: format!(
                        "ALO denominator is too small at row {i}: a_ii={:.6e}, 1-a_ii={:.6e}, min={:.1e}",
                        aii[i], denom_raw, ALO_DENOMINATOR_MIN
                    ),
                });
            }
            let one_step = alo_eta_updatewith_offset(
                eta_hat[i],
                z[i],
                offset[i],
                x_hinv_x_diag[i],
                w_s[i],
                denom_raw,
            );
            // When the family score/curvature evaluator is supplied, solve the
            // exact frozen-curvature leave-i-out fixed point (anchored at η̂_i,
            // the basin that limits to the in-sample fit) instead of taking the
            // single Newton step. a_ii here is the unweighted influence
            // x_i^T H^{-1} x_i (= x_hinv_x_diag[i]); the per-row curvature
            // W_H[i] = ℓ_i''(η̂_i) is folded into the scalar fixed point via
            // score_curvature. Non-canonical links fall back to `one_step`.
            let v = if let Some(score_curvature) = input.score_curvature {
                alo_eta_exact_frozen_curvature(
                    eta_hat[i],
                    x_hinv_x_diag[i],
                    &|eta| score_curvature(i, eta),
                )
                .map_err(|err| AloError::LooComputationFailed {
                    reason: format!(
                        "ALO exact frozen-curvature solve failed at row {i}: {err}"
                    ),
                })?
            } else {
                one_step
            };
            if !v.is_finite() {
                return Err(AloError::LooComputationFailed {
                    reason: format!("ALO eta_tilde is not finite at row {i}: eta_tilde={v}"),
                });
            }
            Ok(v)
        })
        .collect::<Result<_, _>>()?;
    let eta_tilde = Array1::from(eta_tilde_vec);

    Ok(AloDiagnostics {
        eta_tilde,
        se_bayes,
        se_sandwich,
        pred_identity: eta_hat.clone(),
        leverage: aii,
        fisherweights: w_h.to_owned(),
    })
}

fn validate_alo_solve_setup(input: &AloInput, n: usize, p: usize) -> Result<(), AloError> {
    let h = input.penalized_hessian;
    if h.nrows() != p || h.ncols() != p {
        return Err(AloError::InvalidInput {
            reason: format!(
                "ALO diagnostics require a dense exact penalized Hessian with shape {p}x{p}; got {}x{}",
                h.nrows(),
                h.ncols()
            ),
        });
    }
    if h.iter().any(|v| !v.is_finite()) {
        return Err(AloError::InvalidInput {
            reason: "ALO diagnostics require a finite dense exact penalized Hessian".to_string(),
        });
    }
    for i in 0..p {
        for j in 0..i {
            let a = h[[i, j]];
            let b = h[[j, i]];
            let scale = a.abs().max(b.abs()).max(1.0);
            if (a - b).abs() > HESSIAN_SYMMETRY_REL_TOL * scale {
                return Err(AloError::InvalidInput {
                    reason: format!(
                        "ALO diagnostics require a symmetric dense exact penalized Hessian; entries ({i},{j}) and ({j},{i}) differ by {:.3e}",
                        (a - b).abs()
                    ),
                });
            }
        }
    }

    let vector_lengths = [
        ("hessian_weights", input.hessian_weights.len()),
        ("score_weights", input.score_weights.len()),
        ("working_response", input.working_response.len()),
        ("eta", input.eta.len()),
        ("offset", input.offset.len()),
    ];
    for (name, len) in vector_lengths {
        if len != n {
            return Err(AloError::InvalidInput {
                reason: format!("ALO diagnostics require {name} length {n}; got {len}"),
            });
        }
    }
    if input.hessian_weights.view().iter().any(|v| !v.is_finite()) {
        return Err(AloError::WeightInvalid {
            reason: "ALO diagnostics require finite Hessian-side weights".to_string(),
        });
    }
    if input.score_weights.view().iter().any(|v| !v.is_finite()) {
        return Err(AloError::WeightInvalid {
            reason: "ALO diagnostics require finite score-side weights".to_string(),
        });
    }
    if input.working_response.iter().any(|v| !v.is_finite()) {
        return Err(AloError::WeightInvalid {
            reason: "ALO diagnostics require finite working responses".to_string(),
        });
    }
    if input.eta.iter().any(|v| !v.is_finite()) || input.offset.iter().any(|v| !v.is_finite()) {
        return Err(AloError::InvalidInput {
            reason: "ALO diagnostics require finite linear predictors and offsets".to_string(),
        });
    }
    if !input.phi.is_finite() || input.phi <= 0.0 {
        return Err(AloError::InvalidInput {
            reason: format!(
                "ALO diagnostics require positive finite dispersion phi; got {}",
                input.phi
            ),
        });
    }
    if !input.ridge.is_finite() || input.ridge < 0.0 {
        return Err(AloError::InvalidInput {
            reason: format!(
                "ALO diagnostics require a finite non-negative Hessian ridge; got {}",
                input.ridge
            ),
        });
    }
    if let Some(e) = input.penalty_root {
        if e.ncols() != p {
            return Err(AloError::InvalidInput {
                reason: format!(
                    "ALO diagnostics require penalty root to have {p} columns; got {}",
                    e.ncols()
                ),
            });
        }
        if e.iter().any(|v| !v.is_finite()) {
            return Err(AloError::InvalidInput {
                reason: "ALO diagnostics require finite penalty-root entries".to_string(),
            });
        }
    }
    Ok(())
}

/// Compute ALO diagnostics (eta_tilde, SE, leverage) from a fitted GAM result.
pub fn compute_alo_diagnostics_from_fit(
    fit: &UnifiedFitResult,
    y: ArrayView1<f64>,
    link: LinkFunction,
) -> Result<AloDiagnostics, EstimationError> {
    let pirls = fit
        .artifacts
        .pirls
        .as_ref()
        .ok_or_else(|| AloError::InvalidInput {
            reason:
                "ALO diagnostics require a PIRLS-backed fit; this fit does not expose PIRLS geometry"
                    .to_string(),
        })
        .map_err(EstimationError::from)?;
    compute_alo_diagnostics_from_pirls_impl(pirls, y, link)
}

/// Compute ALO diagnostics from a `UnifiedFitResult`.
///
/// Extracts `FitGeometry` from `unified.geometry`, builds an `AloInput`
/// via `from_geometry`, and delegates to `compute_alo_from_input`.
/// This avoids requiring a full `UnifiedFitResult` with PIRLS artifacts.
pub fn compute_alo_diagnostics_from_unified(
    unified: &UnifiedFitResult,
    design: &Array2<f64>,
    eta: &Array1<f64>,
    offset: &Array1<f64>,
    link: LinkFunction,
    phi: f64,
) -> Result<AloDiagnostics, EstimationError> {
    let geom = unified
        .geometry
        .as_ref()
        .ok_or_else(|| AloError::InvalidInput {
            reason: "UnifiedFitResult does not contain working-set geometry; \
             ALO diagnostics require geometry at convergence"
                .to_string(),
        })
        .map_err(EstimationError::from)?;
    let input = AloInput::from_geometry(geom, design, eta, offset, link, phi);
    compute_alo_from_input(&input)
}

/// Compute ALO diagnostics from a PIRLS result for lower-level callers.
pub fn compute_alo_diagnostics_from_pirls(
    base: &pirls::PirlsResult,
    y: ArrayView1<f64>,
    link: LinkFunction,
) -> Result<AloDiagnostics, EstimationError> {
    compute_alo_diagnostics_from_pirls_impl(base, y, link)
}

/// Exact (one-step) case-deletion influence from a converged PIRLS fit, via
/// the one `FitSensitivity` operator (#935).
///
/// This is the diagnostic the sensitivity operator's `case_deletion` channel
/// was built to expose but had no production entry point for: per-observation
/// dfbetas `β̂ − β̂₍ᵢ₎`, hat-value leverage `h_ii = w_i x_iᵀ H⁻¹ x_i`, and
/// Cook's distance. It is the same factored inverse the REML gradient (IFT),
/// ALO, and the Riesz debias already contract — built once at the optimum,
/// asked in the leave-one-out direction — so no call site can disagree about
/// which `H⁻¹` is meant (the bug class #935 dismantles).
///
/// The penalized Hessian, design, working weights `w_i = W_H[i]` and working
/// residual `z_i − η̂_i` are read straight from the converged geometry — the
/// same PIRLS state [`compute_alo_diagnostics_from_pirls`] consumes — so the
/// IRLS reduction `scale = w_i r_i / (1 − h_ii)` is exact for the Gaussian
/// identity link and the one-step Newton deletion for canonical-link GLMs.
/// Returns `None` (rather than emitting `∞`) for any observation whose
/// leverage is one, or if the dense Hessian / design is unavailable.
pub fn compute_case_deletion_from_pirls(
    base: &pirls::PirlsResult,
    y: ArrayView1<f64>,
    link: LinkFunction,
) -> Result<Option<crate::sensitivity::CaseDeletionInfluence>, EstimationError> {
    let x_dense_arc = base
        .x_transformed
        .try_to_dense_arc("case-deletion diagnostics require dense transformed design")
        .map_err(|reason| EstimationError::InvalidInput(reason))?;
    let x_dense = x_dense_arc.as_ref();
    let n = x_dense.nrows();
    let p = x_dense.ncols();
    if n == 0 || p == 0 {
        return Ok(None);
    }

    // Dispersion φ matches the ALO entry point: estimated RSS/(n−edf) for the
    // Gaussian identity link, fixed at 1 for the single-parameter families.
    let phi = match link {
        LinkFunction::Identity => {
            use rayon::iter::{IntoParallelIterator, ParallelIterator};
            let rss: f64 = (0..n)
                .into_par_iter()
                .map(|i| {
                    let r = y[i] - base.finalmu[i];
                    base.finalweights[i] * r * r
                })
                .sum();
            let dof = (n as f64) - base.edf;
            rss / dof.max(1.0)
        }
        _ => 1.0,
    };
    if !(phi.is_finite() && phi > 0.0) {
        return Ok(None);
    }

    // The same dense stabilized penalized Hessian ALO materializes; the one
    // factored inverse every sensitivity channel shares.
    let h_dense = base
        .dense_stabilizedhessian_transformed(
            "case-deletion diagnostics require exact dense stabilized penalized Hessian",
        )
        .map_err(|e| match e {
            EstimationError::InvalidInput(reason) => EstimationError::InvalidInput(reason),
            other => EstimationError::InvalidInput(format!("{other:?}")),
        })?;

    let factor = match h_dense.cholesky(faer::Side::Lower) {
        Ok(f) => f,
        // A non-SPD stabilized Hessian means the optimum is rank-deficient in a
        // way the dense Cholesky case-deletion path cannot invert; decline
        // rather than fabricate an influence diagnostic.
        Err(_) => return Ok(None),
    };

    // Working weights and working residual straight from the IRLS reduction:
    // w_i = W_H[i] and r_i = z_i − η̂_i, so w_i r_i is the working score the
    // closed-form deletion `scale = w_i r_i / (1 − h_ii)` consumes.
    let working_weights = base.finalweights.clone();
    let working_residual = &base.solveworking_response - &base.final_eta;

    let sensitivity = crate::sensitivity::FitSensitivity::from_faer_cholesky(&factor, p);
    Ok(sensitivity.case_deletion(
        x_dense,
        working_weights.view(),
        working_residual.view(),
        phi,
    ))
}

// Multi-block ALO for multi-predictor models (GAMLSS, survival, joint)

/// Diagnostics returned by multi-block ALO.
#[derive(Debug, Clone)]
pub struct MultiBlockAloDiagnostics {
    /// Corrected linear predictors η̃^{(-i)} for each observation.
    /// Outer length = n_obs, inner length = n_blocks (B).
    pub eta_tilde: Vec<Array1<f64>>,
    /// Per-observation leverage tr(H_ii) where H_ii is the B×B hat-matrix block.
    pub leverage: Array1<f64>,
    /// Per-observation ALO variance diagonals: for each observation i,
    /// Var(Δη_i) ≈ A_i (I - W_i A_i)⁻¹ W_i (I - A_i W_i)⁻¹ A_iᵀ.
    /// Outer length = n_obs, inner length = n_blocks (B) containing the
    /// diagonal entries of the variance matrix.
    pub alo_variance: Vec<Array1<f64>>,
    /// Cook-type ALO influence: D_i = Δη_iᵀ W_i Δη_i.
    /// Length = n_obs.
    pub cook_distance: Array1<f64>,
}

/// Model-agnostic input for multi-predictor ALO diagnostics.
///
/// Generalises [`AloInput`] to models with B > 1 linear predictors per
/// observation (e.g. location-scale GAMLSS with B=2, or survival models
/// with time-dependent predictors).
///
/// # Mathematical setup
///
/// For observation i the per-observation Jacobian is a B × p_tot block matrix
/// X_i whose b-th row is the i-th row of `block_designs[b]`.  The joint
/// hat-matrix block is
///
///   H_ii = X_i H⁻¹ X_iᵀ W_i     (B × B)
///
/// where H = Σ_i X_iᵀ W_i X_i + S is the total penalized Hessian and W_i
/// is the B × B per-observation weight matrix (negative Hessian of the
/// log-likelihood w.r.t. the B predictors at observation i).
///
/// The ALO leave-one-out correction is
///
///   Δη_i^ALO = A_i (I_B − W_i A_i)⁻¹ s_i
///
/// where A_i = X_i H⁻¹ X_iᵀ (the B×B per-observation influence matrix),
/// W_i is the B×B per-observation NLL Hessian, and
/// s_i = ∇_{η_i} NLL_i(η̂_i) is the B-dimensional score vector.
/// This is algebraically equivalent to (I_B − H_ii)⁻¹ H_ii W_i⁻¹ s_i
/// but does NOT require W_i⁻¹, which is critical when W_i is singular
/// (e.g. at boundary observations in survival models).
/// For B = 1 this reduces to the classical scalar ALO formula.
pub struct MultiBlockAloInput<'a> {
    /// Number of observations.
    pub n_obs: usize,
    /// Number of predictors per observation (B).
    pub n_blocks: usize,
    /// B design matrices, each n_obs × p_b.  The total parameter count is
    /// p_tot = Σ_b p_b.
    pub block_designs: &'a [Array2<f64>],
    /// Inverse of the penalized Hessian, H⁻¹ (p_tot × p_tot).
    pub penalized_hessian_inv: &'a Array2<f64>,
    /// Per-observation weight matrices W_i (B × B).  Length = n_obs.
    pub block_weights: Vec<Array2<f64>>,
    /// Per-observation score vectors s_i = ∇_{η_i} NLL_i.  Length = n_obs,
    /// each entry is B-dimensional.
    pub scores: Vec<Array1<f64>>,
    /// Fitted linear predictor vectors η̂_i.  Length = n_obs, each entry is
    /// B-dimensional.
    pub eta_hat: Vec<Array1<f64>>,
}

/// Compute multi-block ALO diagnostics: corrected η̃ and leverages.
///
/// # Optimisation note
///
/// The dominant cost is forming X_i H⁻¹ X_iᵀ for every observation.
/// Rather than forming the B × p_tot row-block X_i and multiplying naïvely,
/// we precompute for each block b the matrix
///
///   Q_b = H⁻¹ X_bᵀ      (p_tot × n)
///
/// Then the (a, b) entry of the B × B matrix X_i H⁻¹ X_iᵀ is simply
///
///   (X_i H⁻¹ X_iᵀ)_{a,b} = x_{a,i}ᵀ Q_b[:,i]
///                           = Σ_k  X_a[i,k] · Q_b[k,i]
///
/// where x_{a,i} is the i-th row of block-design a.  This turns the per-
/// observation work from O(B · p_tot²) into O(B² · p_tot), and the
/// precomputation is O(B · p_tot² · n) total via a single blocked solve.
pub fn compute_multiblock_alo(
    input: &MultiBlockAloInput,
) -> Result<MultiBlockAloDiagnostics, EstimationError> {
    compute_multiblock_alo_inner(input).map_err(EstimationError::from)
}

fn compute_multiblock_alo_inner(
    input: &MultiBlockAloInput,
) -> Result<MultiBlockAloDiagnostics, AloError> {
    use rayon::prelude::*;

    let n = input.n_obs;
    let b = input.n_blocks;
    let p_tot = input.penalized_hessian_inv.nrows();

    // --- Validate dimensions ---
    if input.block_designs.len() != b {
        return Err(AloError::InvalidInput {
            reason: format!(
                "MultiBlockAloInput: expected {} block designs, got {}",
                b,
                input.block_designs.len()
            ),
        });
    }

    // Verify total column count matches p_tot.
    let col_sum: usize = input.block_designs.iter().map(|d| d.ncols()).sum();
    if col_sum != p_tot {
        return Err(AloError::InvalidInput {
            reason: format!(
                "MultiBlockAloInput: total design columns ({}) != penalized_hessian_inv size ({})",
                col_sum, p_tot
            ),
        });
    }

    let col_offsets = multiblock_col_offsets(input.block_designs);
    let (chunk_size, max_concurrent_chunks) = multiblock_alo_parallel_plan(p_tot, b, n);
    let chunk_starts: Vec<usize> = (0..n).step_by(chunk_size).collect();

    // Each Rayon worker owns its small B×B/B-vector scratch buffers via
    // `map_init`, avoiding cross-thread mutation and avoiding per-observation
    // allocations.  The much larger Q panels are bounded by the parallel chunk
    // size and by wave-level concurrency, so at most roughly one global memory
    // budget worth of p_total × chunk_len panels can be live across workers.
    let mut chunk_results: Vec<Result<MultiBlockAloChunkDiagnostics, AloError>> =
        Vec::with_capacity(chunk_starts.len());
    for chunk_wave in chunk_starts.chunks(max_concurrent_chunks) {
        let mut wave_results: Vec<Result<MultiBlockAloChunkDiagnostics, AloError>> = chunk_wave
            .par_iter()
            .map_init(
                || MultiBlockAloScratch::new(b),
                |scratch, &chunk_start| {
                    let chunk_end = (chunk_start + chunk_size).min(n);
                    compute_multiblock_alo_chunk(
                        input,
                        &col_offsets,
                        chunk_start,
                        chunk_end,
                        scratch,
                    )
                },
            )
            .collect();
        chunk_results.append(&mut wave_results);
    }

    let mut eta_tilde = Vec::with_capacity(n);
    let mut leverage = Array1::<f64>::zeros(n);
    let mut alo_variance = Vec::with_capacity(n);
    let mut cook_distance = Array1::<f64>::zeros(n);

    let mut chunks = Vec::with_capacity(chunk_results.len());
    for result in chunk_results {
        chunks.push(result?);
    }
    chunks.sort_unstable_by_key(|chunk| chunk.chunk_start);

    for chunk in chunks {
        let chunk_start = chunk.chunk_start;
        eta_tilde.extend(chunk.eta_tilde);
        alo_variance.extend(chunk.alo_variance);
        for (local_i, lev) in chunk.leverage.into_iter().enumerate() {
            leverage[chunk_start + local_i] = lev;
        }
        for (local_i, cook) in chunk.cook_distance.into_iter().enumerate() {
            cook_distance[chunk_start + local_i] = cook;
        }
    }

    Ok(MultiBlockAloDiagnostics {
        eta_tilde,
        leverage,
        alo_variance,
        cook_distance,
    })
}

#[inline]
fn multiblock_alo_parallel_plan(p_tot: usize, n_blocks: usize, n_obs: usize) -> (usize, usize) {
    if p_tot == 0 || n_blocks == 0 || n_obs == 0 {
        return (1, 1);
    }
    let bytes_per_obs = (p_tot * n_blocks * std::mem::size_of::<f64>()).max(1);
    let workers = rayon::current_num_threads().max(1);
    let max_concurrent_chunks = (MULTIBLOCK_ALO_MEMORY_BUDGET_BYTES / bytes_per_obs)
        .max(1)
        .min(workers);
    let per_worker_budget =
        (MULTIBLOCK_ALO_MEMORY_BUDGET_BYTES / max_concurrent_chunks).max(bytes_per_obs);
    let budget_obs = (per_worker_budget / bytes_per_obs).max(1);
    (budget_obs.min(n_obs), max_concurrent_chunks)
}

struct MultiBlockAloScratch {
    a_i: Vec<f64>,
    wa: Vec<f64>,
    aw: Vec<f64>,
    imwa: Vec<f64>,
    imaw: Vec<f64>,
    perm_imwa: Vec<usize>,
    perm_imaw: Vec<usize>,
    delta_eta: Vec<f64>,
    rhs_buf: Vec<f64>,
    w_u: Vec<f64>,
    var_diag_buf: Vec<f64>,
    w_flat: Vec<f64>,
    lu_scratch: Vec<f64>,
}

impl MultiBlockAloScratch {
    fn new(b: usize) -> Self {
        let bb_sz = b * b;
        Self {
            a_i: vec![0.0f64; bb_sz],
            wa: vec![0.0f64; bb_sz],
            aw: vec![0.0f64; bb_sz],
            imwa: vec![0.0f64; bb_sz],
            imaw: vec![0.0f64; bb_sz],
            perm_imwa: vec![0usize; b],
            perm_imaw: vec![0usize; b],
            delta_eta: vec![0.0f64; b],
            rhs_buf: vec![0.0f64; b],
            w_u: vec![0.0f64; b],
            var_diag_buf: vec![0.0f64; b],
            w_flat: vec![0.0f64; bb_sz],
            lu_scratch: vec![0.0f64; b],
        }
    }
}

struct MultiBlockAloChunkDiagnostics {
    chunk_start: usize,
    eta_tilde: Vec<Array1<f64>>,
    leverage: Vec<f64>,
    alo_variance: Vec<Array1<f64>>,
    cook_distance: Vec<f64>,
}

fn compute_multiblock_alo_chunk(
    input: &MultiBlockAloInput,
    col_offsets: &[usize],
    chunk_start: usize,
    chunk_end: usize,
    scratch: &mut MultiBlockAloScratch,
) -> Result<MultiBlockAloChunkDiagnostics, AloError> {
    let b = input.n_blocks;
    let chunk_len = chunk_end - chunk_start;

    let mut q_blocks = Vec::with_capacity(b);
    for blk in 0..b {
        let x_chunk_t = input.block_designs[blk]
            .slice(s![chunk_start..chunk_end, ..])
            .t()
            .to_owned();
        let off_b = col_offsets[blk];
        let h_slice = input
            .penalized_hessian_inv
            .slice(s![.., off_b..off_b + x_chunk_t.nrows()])
            .to_owned();
        q_blocks.push(h_slice.dot(&x_chunk_t));
    }

    let mut eta_tilde = Vec::with_capacity(chunk_len);
    let mut leverage = vec![0.0f64; chunk_len];
    let mut alo_variance = Vec::with_capacity(chunk_len);
    let mut cook_distance = vec![0.0f64; chunk_len];

    for local_i in 0..chunk_len {
        let i = chunk_start + local_i;
        let w_i = &input.block_weights[i];

        // Flatten W_i once per observation (row-major).
        for r in 0..b {
            for c in 0..b {
                scratch.w_flat[r * b + c] = w_i[(r, c)];
            }
        }

        // --- Assemble A_i = X_i H⁻¹ X_iᵀ  (B × B), row-major flat. ---
        for a in 0..b {
            let x_a = &input.block_designs[a];
            let p_a = x_a.ncols();
            let off_a = col_offsets[a];
            let xa_row = x_a.row(i);
            for bb in 0..b {
                let q_bb = &q_blocks[bb];
                let mut dot = 0.0f64;
                for k in 0..p_a {
                    dot += xa_row[k] * q_bb[(off_a + k, local_i)];
                }
                scratch.a_i[a * b + bb] = dot;
            }
        }

        // WA = W_i · A_i (row-major).
        mat_mul_flat(&scratch.w_flat, &scratch.a_i, &mut scratch.wa, b);
        // AW = A_i · W_i (row-major).
        mat_mul_flat(&scratch.a_i, &scratch.w_flat, &mut scratch.aw, b);

        // Trace of H_ii = A_i W_i (= AW): leverage[i].
        // (Original code wrote H_ii = A · W — the same operator we already have in `aw`.)
        let mut tr = 0.0f64;
        for d in 0..b {
            tr += scratch.aw[d * b + d];
        }
        leverage[local_i] = tr;

        // Build (I - W A) and (I - A W) into imwa/imaw.
        for r in 0..b {
            for c in 0..b {
                let idx = r * b + c;
                let id = if r == c { 1.0 } else { 0.0 };
                scratch.imwa[idx] = id - scratch.wa[idx];
                scratch.imaw[idx] = id - scratch.aw[idx];
            }
        }

        // Factor in place with partial pivoting; ridge on the diagonal if singular.
        // Equivalence with original: original computed det via det_small, regularized
        // by adding eps=1e-6 to the diagonal when |det| < 1e-12, then re-factored on
        // the regularized matrix. Here we factor directly; if any pivot is below the
        // singular threshold we add the ridge once and re-factor — same numerical path.
        if !lu_factor_in_place(&mut scratch.imwa, &mut scratch.perm_imwa, b) {
            for r in 0..b {
                for c in 0..b {
                    let idx = r * b + c;
                    let id = if r == c { 1.0 } else { 0.0 };
                    scratch.imwa[idx] = id - scratch.wa[idx];
                }
            }
            for d in 0..b {
                scratch.imwa[d * b + d] += ALO_LOCAL_BLOCK_RIDGE;
            }
            let refactored = lu_factor_in_place(&mut scratch.imwa, &mut scratch.perm_imwa, b);
            assert!(
                refactored,
                "ALO local block remained singular after ridge regularization"
            );
        }
        if !lu_factor_in_place(&mut scratch.imaw, &mut scratch.perm_imaw, b) {
            for r in 0..b {
                for c in 0..b {
                    let idx = r * b + c;
                    let id = if r == c { 1.0 } else { 0.0 };
                    scratch.imaw[idx] = id - scratch.aw[idx];
                }
            }
            for d in 0..b {
                scratch.imaw[d * b + d] += ALO_LOCAL_BLOCK_RIDGE;
            }
            let refactored = lu_factor_in_place(&mut scratch.imaw, &mut scratch.perm_imaw, b);
            assert!(
                refactored,
                "ALO local variance block remained singular after ridge regularization"
            );
        }

        // v_i = (I - W A)⁻¹ s_i  -- solve into rhs_buf.
        let s_i = &input.scores[i];
        for k in 0..b {
            scratch.rhs_buf[k] = s_i[k];
        }
        lu_solve_in_place(
            &scratch.imwa,
            &scratch.perm_imwa,
            &mut scratch.rhs_buf,
            &mut scratch.lu_scratch,
            b,
        );
        // delta_eta = A_i · v_i
        for r in 0..b {
            let mut acc = 0.0f64;
            let row_off = r * b;
            for k in 0..b {
                acc += scratch.a_i[row_off + k] * scratch.rhs_buf[k];
            }
            scratch.delta_eta[r] = acc;
        }

        let eta_i = &input.eta_hat[i];
        let mut corrected = Array1::<f64>::zeros(b);
        for d in 0..b {
            corrected[d] = eta_i[d] + scratch.delta_eta[d];
        }
        eta_tilde.push(corrected);

        // Cook's distance: δη^T W δη.
        let mut cook = 0.0f64;
        for r in 0..b {
            let mut w_delta_r = 0.0f64;
            let row_off = r * b;
            for k in 0..b {
                w_delta_r += scratch.w_flat[row_off + k] * scratch.delta_eta[k];
            }
            cook += scratch.delta_eta[r] * w_delta_r;
        }
        cook_distance[local_i] = cook;

        // var_diag[d] = a_d^T (I-WA)⁻¹ W (I-AW)⁻¹ a_d
        // where a_d is the d-th row of A_i.
        // Reuses already-factored imwa and imaw (one LU factorization each, reused
        // across all B right-hand sides — major saving over the original which redid
        // both LU decompositions B times per observation).
        for d in 0..b {
            let row_off = d * b;
            // u_d = (I - A W)⁻¹ a_d
            for k in 0..b {
                scratch.rhs_buf[k] = scratch.a_i[row_off + k];
            }
            lu_solve_in_place(
                &scratch.imaw,
                &scratch.perm_imaw,
                &mut scratch.rhs_buf,
                &mut scratch.lu_scratch,
                b,
            );
            // w_u = W u_d
            for r in 0..b {
                let mut acc = 0.0f64;
                let wr = r * b;
                for k in 0..b {
                    acc += scratch.w_flat[wr + k] * scratch.rhs_buf[k];
                }
                scratch.w_u[r] = acc;
            }
            // t_d = (I - W A)⁻¹ w_u  (back-solve in place using w_u as RHS).
            lu_solve_in_place(
                &scratch.imwa,
                &scratch.perm_imwa,
                &mut scratch.w_u,
                &mut scratch.lu_scratch,
                b,
            );
            // v_dd = a_d^T t_d
            let mut v_dd = 0.0f64;
            for k in 0..b {
                v_dd += scratch.a_i[row_off + k] * scratch.w_u[k];
            }
            scratch.var_diag_buf[d] = v_dd.max(0.0);
        }
        let mut var_diag = Array1::<f64>::zeros(b);
        for d in 0..b {
            var_diag[d] = scratch.var_diag_buf[d];
        }
        alo_variance.push(var_diag);
    }

    Ok(MultiBlockAloChunkDiagnostics {
        chunk_start,
        eta_tilde,
        leverage,
        alo_variance,
        cook_distance,
    })
}

/// B × B row-major matmul: out = a · b.
#[inline]
fn mat_mul_flat(a: &[f64], b_mat: &[f64], out: &mut [f64], b: usize) {
    for r in 0..b {
        let ar = r * b;
        let or = r * b;
        for c in 0..b {
            let mut acc = 0.0f64;
            for k in 0..b {
                acc += a[ar + k] * b_mat[k * b + c];
            }
            out[or + c] = acc;
        }
    }
}

/// LU-decompose a B × B row-major matrix in place with partial pivoting and
/// physical row swaps. Returns false if any pivot |a_kk| < 1e-12 (singular).
/// On success, `m` holds L (strict lower, unit diag implicit) and U (upper, diag
/// included); `perm[k]` records the original-row index that ended up in physical
/// row k after pivoting. Pivot threshold matches the original `det_small < 1e-12`
/// path so the regularization branch fires under equivalent conditions.
fn lu_factor_in_place(m: &mut [f64], perm: &mut [usize], b: usize) -> bool {
    for i in 0..b {
        perm[i] = i;
    }
    for col in 0..b {
        // Partial pivot on column `col` over physical rows `[col..b]`.
        let mut max_val = m[col * b + col].abs();
        let mut max_idx = col;
        for row in (col + 1)..b {
            let v = m[row * b + col].abs();
            if v > max_val {
                max_val = v;
                max_idx = row;
            }
        }
        if max_val < LU_PIVOT_SINGULAR_TOL {
            return false;
        }
        if max_idx != col {
            // Physically swap rows `col` and `max_idx` (full row, all columns).
            for k in 0..b {
                m.swap(col * b + k, max_idx * b + k);
            }
            perm.swap(col, max_idx);
        }
        let pivot = m[col * b + col];
        for row in (col + 1)..b {
            let factor = m[row * b + col] / pivot;
            m[row * b + col] = factor; // store L below diag
            for k in (col + 1)..b {
                let upd = factor * m[col * b + k];
                m[row * b + k] -= upd;
            }
        }
    }
    true
}

/// Solve L U x = P rhs using a previously factored matrix (LU in `m`, perm).
/// Writes the solution back into `rhs`. `scratch` must have length ≥ b.
fn lu_solve_in_place(m: &[f64], perm: &[usize], rhs: &mut [f64], scratch: &mut [f64], b: usize) {
    // Forward substitution Ly = P rhs (L is unit-diag, strict lower of m).
    let y = &mut scratch[..b];
    for row in 0..b {
        let mut s = rhs[perm[row]];
        for k in 0..row {
            s -= m[row * b + k] * y[k];
        }
        y[row] = s;
    }
    // Back substitution U x = y.  Write into rhs[].
    for row in (0..b).rev() {
        let mut s = y[row];
        for k in (row + 1)..b {
            s -= m[row * b + k] * rhs[k];
        }
        rhs[row] = s / m[row * b + row];
    }
}

/// Compute only per-observation leverages tr(H_ii) for multi-predictor models.
///
/// This is cheaper than the full ALO correction when only EDF or leverage
/// diagnostics are needed (no scores or W⁻¹ computation required).
///
/// Returns an n-length array of leverages.  The total model EDF is the sum
/// of all leverages.
pub fn compute_multiblock_alo_leverages(
    n_obs: usize,
    n_blocks: usize,
    block_designs: &[Array2<f64>],
    penalized_hessian_inv: &Array2<f64>,
    block_weights: &[Array2<f64>],
) -> Result<Array1<f64>, EstimationError> {
    use rayon::prelude::*;

    let n = n_obs;
    let b = n_blocks;
    let p_tot = penalized_hessian_inv.nrows();

    let col_offsets = multiblock_col_offsets(block_designs);
    let max_workers = rayon::current_num_threads();
    let chunk_size = multiblock_alo_parallel_leverage_chunk_size(p_tot, b, n, max_workers);

    let mut leverage = Array1::<f64>::zeros(n);

    // Per-block H_inv stripe scratch (p_tot × p_blk) is read-only once built
    // and shared by the parallel chunks.  Only per-chunk q/XT/B×B scratch is
    // replicated across Rayon workers.
    let block_widths: Vec<usize> = block_designs.iter().map(|d| d.ncols()).collect();
    let mut h_stripes: Vec<FaerMat<f64>> = block_widths
        .iter()
        .map(|&p_blk| FaerMat::<f64>::zeros(p_tot, p_blk))
        .collect();
    // Populate the H_inv stripes once: each block reads a constant column slab
    // out of `penalized_hessian_inv` and copies it into a column-major faer Mat.
    for blk in 0..b {
        let off_b = col_offsets[blk];
        let p_blk = block_widths[blk];
        let stripe = &mut h_stripes[blk];
        for c in 0..p_blk {
            for r in 0..p_tot {
                stripe[(r, c)] = penalized_hessian_inv[(r, off_b + c)];
            }
        }
    }

    leverage
        .as_slice_mut()
        .expect("newly allocated Array1 is contiguous")
        .par_chunks_mut(chunk_size)
        .enumerate()
        .for_each(|(chunk_idx, leverage_chunk)| {
            let chunk_start = chunk_idx * chunk_size;
            let chunk_len = leverage_chunk.len();
            let chunk_end = chunk_start + chunk_len;

            // Chunk-local scratch: B×B flat row-major buffers for A_i, W_i
            // and AW = A·W.  Each worker writes only its `leverage_chunk`, so
            // output writes are disjoint and require no synchronization.
            let bb_sz = b * b;
            let mut a_i = vec![0.0f64; bb_sz];
            let mut aw = vec![0.0f64; bb_sz];
            let mut w_flat = vec![0.0f64; bb_sz];

            // Column-major faer storage for q_blocks: q_k has shape
            // (p_tot, chunk_len) with contiguous columns, so
            // `col_as_slice(local_i)` is a direct stripe.
            let mut q_storage: Vec<FaerMat<f64>> = block_widths
                .iter()
                .map(|_| FaerMat::<f64>::zeros(p_tot, chunk_len))
                .collect();

            // Per-block X^T scratch in column-major faer storage
            // (p_blk × chunk_len), owned by this chunk to keep the matmul input
            // contiguous without sharing mutable scratch across threads.
            let mut xt_storage: Vec<FaerMat<f64>> = block_widths
                .iter()
                .map(|&p_blk| FaerMat::<f64>::zeros(p_blk, chunk_len))
                .collect();

            // Build q_blocks[blk] = H_inv[:, off..off+p_blk] · X_blk[chunk, :]^T
            // entirely in column-major faer storage so subsequent column reads
            // are contiguous f64 stripes — replaces the per-chunk `to_owned()`
            // ndarray slicing + row-major `dot()` from the original.
            for blk in 0..b {
                let p_blk = block_widths[blk];

                let x_chunk = block_designs[blk].slice(s![chunk_start..chunk_end, ..]);
                let xt = &mut xt_storage[blk];
                for local_i in 0..chunk_len {
                    let row = x_chunk.row(local_i);
                    for j in 0..p_blk {
                        xt[(j, local_i)] = row[j];
                    }
                }

                matmul(
                    q_storage[blk].as_mut(),
                    Accum::Replace,
                    h_stripes[blk].as_ref(),
                    xt_storage[blk].as_ref(),
                    1.0,
                    Par::Seq,
                );
            }

            for local_i in 0..chunk_len {
                let i = chunk_start + local_i;
                let w_i = &block_weights[i];

                // Flatten W_i once per observation (row-major).
                for r in 0..b {
                    for c in 0..b {
                        w_flat[r * b + c] = w_i[(r, c)];
                    }
                }

                // Assemble A_i[a, k] = X_a[i, :] · q_k[off_a:off_a+p_a, local_i].
                // For each k, read its column once (contiguous f64 stripe), then
                // for each a take the matching offset slab.
                for r in 0..bb_sz {
                    a_i[r] = 0.0;
                }
                for k in 0..b {
                    let q_k = &q_storage[k];
                    let q_col = q_k.col_as_slice(local_i);
                    for a in 0..b {
                        let p_a = block_widths[a];
                        let off_a = col_offsets[a];
                        let xa_row = block_designs[a].row(i);
                        let mut dot = 0.0f64;
                        for j in 0..p_a {
                            dot = xa_row[j].mul_add(q_col[off_a + j], dot);
                        }
                        a_i[a * b + k] = dot;
                    }
                }

                // AW = A_i · W_i (B×B), then leverage = trace(AW) = sum_{a,k} A[a,k]·W[k,a].
                mat_mul_flat(&a_i, &w_flat, &mut aw, b);
                let mut tr = 0.0f64;
                for d in 0..b {
                    tr += aw[d * b + d];
                }
                leverage_chunk[local_i] = tr;
            }
        });

    Ok(leverage)
}

// (Allocation-free, factor-once-reuse-many B×B LU helpers live next to the
// multi-block ALO callsite — see `lu_factor_in_place` and `lu_solve_in_place`.)

#[cfg(test)]
mod tests {
    use super::{
        ALO_EXACT_SCALAR_MAX_ITERS, AloExactScalarError, AloInput, alo_eta_exact_frozen_curvature,
        alo_eta_updatewith_offset, bayesvar_eta, compute_alo_from_input_inner,
        percentile_from_sorted, percentile_index, sandwichvar_eta_from_meat,
    };
    use gam_linalg::matrix::{PsdWeightsView, SignedWeightsView};
    use gam_problem::LinkFunction;

    #[test]
    fn alo_offset_update_matches_centered_algebra() {
        let eta_hat = 11.0;
        let z = 13.0;
        let offset = 10.0;
        let x_hinv_x = 0.2;
        let hessian_weight = 1.0;
        let score_weight = 1.0;
        // centered: eta~=off + ((eta-off)-a(z-off))/(1-a) when W_S = W_H.
        let leverage = hessian_weight * x_hinv_x;
        let expected = offset + ((eta_hat - offset) - leverage * (z - offset)) / (1.0 - leverage);
        let got =
            alo_eta_updatewith_offset(eta_hat, z, offset, x_hinv_x, score_weight, 1.0 - leverage);
        assert!((got - expected).abs() < 1e-12);
    }

    #[test]
    fn alo_offset_update_reduces_to_classicwhen_offsetzero() {
        let eta_hat = 1.25;
        let z = -0.5;
        let x_hinv_x = 0.35;
        let hessian_weight = 1.0;
        let score_weight = 1.0;
        let leverage = hessian_weight * x_hinv_x;
        let expected = (eta_hat - leverage * z) / (1.0 - leverage);
        let got =
            alo_eta_updatewith_offset(eta_hat, z, 0.0, x_hinv_x, score_weight, 1.0 - leverage);
        assert!((got - expected).abs() < 1e-12);
    }

    #[test]
    fn alo_offset_update_uses_distinct_score_and_hessian_weights() {
        let eta_hat = 1.7;
        let z = 0.4;
        let offset = -0.2;
        let x_hinv_x = 0.15;
        let hessian_weight = 3.0;
        let score_weight = 5.0;
        let expected = offset
            + (eta_hat - offset)
            + x_hinv_x * score_weight * ((eta_hat - offset) - (z - offset))
                / (1.0 - hessian_weight * x_hinv_x);
        let got = alo_eta_updatewith_offset(
            eta_hat,
            z,
            offset,
            x_hinv_x,
            score_weight,
            1.0 - hessian_weight * x_hinv_x,
        );
        assert!((got - expected).abs() < 1e-12);
    }

    #[test]
    fn alo_offset_update_handles_zero_hessian_weight() {
        let eta_hat = 0.8;
        let z = -0.3;
        let offset = 0.1;
        let x_hinv_x = 0.4;
        let hessian_weight = 0.0;
        let score_weight = 2.5;
        let expected = offset
            + (eta_hat - offset)
            + x_hinv_x * score_weight * ((eta_hat - offset) - (z - offset));
        let got = alo_eta_updatewith_offset(
            eta_hat,
            z,
            offset,
            x_hinv_x,
            score_weight,
            1.0 - hessian_weight * x_hinv_x,
        );
        assert!((got - expected).abs() < 1e-12);
    }

    #[test]
    fn alo_exact_frozen_curvature_converges_to_fixed_point() {
        let eta_hat = 1.0;
        let a_ii = 0.4;
        let got = alo_eta_exact_frozen_curvature(eta_hat, a_ii, &|eta| (0.5 * (eta - 2.0), 0.5))
            .expect("linear scalar fixed point should converge in one Newton step");
        assert!((got - 0.75).abs() < 1e-12);
    }

    #[test]
    fn alo_exact_frozen_curvature_reports_nonconvergence() {
        let err = alo_eta_exact_frozen_curvature(0.0, 1.0, &|eta| (eta + 1.0, 0.0))
            .expect_err("constant residual should exhaust the scalar iteration budget");
        let AloExactScalarError::MaxIterations { iterations, .. } = err else {
            panic!("constant residual must report MaxIterations, got {err:?}");
        };
        assert_eq!(
            iterations, ALO_EXACT_SCALAR_MAX_ITERS,
            "non-convergence must report the full scalar iteration budget"
        );
    }

    #[test]
    fn alo_input_reports_exact_scalar_nonconvergence_with_row_context() {
        let design = Array2::from_elem((1, 1), 1.0);
        let penalized_hessian = Array2::from_elem((1, 1), 1.0);
        let hessian_weights = Array1::from_vec(vec![0.0]);
        let score_weights = Array1::from_vec(vec![0.0]);
        let working_response = Array1::from_vec(vec![0.0]);
        let eta = Array1::from_vec(vec![0.0]);
        let offset = Array1::from_vec(vec![0.0]);
        let score_curvature = |_: usize, eta: f64| (eta + 1.0, 0.0);
        let input = AloInput {
            design: &design,
            penalized_hessian: &penalized_hessian,
            hessian_weights: SignedWeightsView::from_array(&hessian_weights),
            score_weights: PsdWeightsView::try_from_array(&score_weights).expect("psd weights"),
            working_response: &working_response,
            eta: &eta,
            offset: &offset,
            link: LinkFunction::Logit,
            phi: 1.0,
            penalty_root: None,
            ridge: 0.0,
            score_curvature: Some(&score_curvature),
        };

        let err =
            compute_alo_from_input_inner(&input).expect_err("non-converged exact ALO must error");
        let msg = err.to_string();
        assert!(
            msg.contains("ALO exact frozen-curvature solve failed at row 0"),
            "missing row context in exact ALO error: {msg}"
        );
        assert!(
            msg.contains("did not converge within"),
            "missing non-convergence cause in exact ALO error: {msg}"
        );
    }

    #[test]
    fn gaussian_unpenalized_direct_sandwich_equals_bayes() {
        // In a Gaussian linear model with H = X'WX, direct meat
        // x_i'H^{-1}X'WXH^{-1}x_i equals x_i'H^{-1}x_i.
        let phi = 2.5;
        let x_hinv_x = 0.3;
        let vb = bayesvar_eta(phi, x_hinv_x);
        let vs = sandwichvar_eta_from_meat(phi, x_hinv_x);
        assert!((vb - vs).abs() < 1e-12);
    }

    #[test]
    fn sandwich_from_direct_meat_scales_by_phi() {
        let phi = 1.7;
        let meat_quad = 0.358;
        let got = sandwichvar_eta_from_meat(phi, meat_quad);
        let expected = phi * meat_quad;
        assert!((got - expected).abs() < 1e-12);
    }

    #[test]
    fn sandwich_meat_uses_score_weights_not_hessian_weights_noncanonical() {
        // Regression for the sandwich-SE "meat" weight bug: the meat must be the
        // SCORE covariance Xᵀ diag(W_S) X (Fisher, PSD), NOT the observed-info
        // Hessian weight W_H (signed). This fixture mimics a non-canonical link
        // (W_H ≠ W_S) with mixed-sign observed curvature.
        //
        // Single column (p = 1) makes H a scalar, so the sandwich variance is
        // closed form: with H = Σ W_H·x² + s0 (> 0 after the penalty), the meat
        // for obs is x_obs²·H⁻²·Σ_row W_S·x_row², and se = sqrt(φ·meat).
        let x = Array2::from_shape_vec((5, 1), vec![1.0, 2.0, 1.0, 2.0, 1.0]).unwrap();
        // Mixed-sign observed-information weights; the negative rows carry the
        // larger design values so Σ W_H·x² is NEGATIVE (see assert below).
        let w_h_vec = Array1::from_vec(vec![1.0, -1.0, 1.0, -1.0, 0.5]);
        // Score/Fisher weights are strictly positive (PSD by construction).
        let w_s_vec = Array1::from_vec(vec![1.0, 0.8, 1.2, 0.6, 0.9]);
        let phi = 1.3;

        let n = x.nrows();
        let sum_wh_x2: f64 = (0..n).map(|i| w_h_vec[i] * x[[i, 0]] * x[[i, 0]]).sum();
        let sum_ws_x2: f64 = (0..n).map(|i| w_s_vec[i] * x[[i, 0]] * x[[i, 0]]).sum();
        // The whole point: Σ W_H·x² < 0 < Σ W_S·x². With W_H the meat is negative
        // and the "materially negative sandwich variance" guard would trip
        // (spurious LooComputationFailed); with W_S it is a valid PSD meat.
        assert!(sum_wh_x2 < 0.0, "fixture must exercise a negative W_H meat");
        assert!(sum_ws_x2 > 0.0);

        // Penalize enough that the penalized Hessian is PD despite Σ W_H·x² < 0.
        let s0 = 8.0_f64;
        let h = s0 + sum_wh_x2; // = 2.5
        assert!(h > 0.0, "penalized Hessian must stay PD");
        let penalized_hessian = Array2::from_elem((1, 1), h);

        // Pre-fix arithmetic check: the OLD W_H meat would be materially negative
        // for the larger-x rows, so the old code returned LooComputationFailed.
        let old_meat_obs1 = x[[1, 0]] * x[[1, 0]] / (h * h) * sum_wh_x2;
        assert!(
            phi * old_meat_obs1 < -super::variance_negative_tolerance(phi * old_meat_obs1.abs()),
            "the pre-fix W_H meat must be materially negative (guard would trip)"
        );

        let working_response = Array1::from_vec(vec![0.3, -0.2, 0.5, 0.1, -0.4]);
        let eta = Array1::from_vec(vec![0.2, 0.1, 0.4, -0.1, 0.05]);
        let offset = Array1::zeros(n);
        let input = AloInput {
            design: &x,
            penalized_hessian: &penalized_hessian,
            hessian_weights: SignedWeightsView::from_array(&w_h_vec),
            score_weights: PsdWeightsView::try_from_array(&w_s_vec).expect("psd weights"),
            working_response: &working_response,
            eta: &eta,
            offset: &offset,
            link: LinkFunction::Probit,
            phi,
            penalty_root: None,
            ridge: 0.0,
            score_curvature: None,
        };

        // The fix must let this succeed (no spurious negative-meat failure)...
        let diag = compute_alo_from_input_inner(&input)
            .expect("fixed sandwich meat (W_S) must not trip the negative-variance guard");

        // ...and match the closed-form W_S reference for every row.
        for obs in 0..n {
            let expected =
                (phi * x[[obs, 0]] * x[[obs, 0]] / (h * h) * sum_ws_x2).sqrt();
            assert!(
                (diag.se_sandwich[obs] - expected).abs() <= 1e-10 * expected.max(1.0),
                "row {obs}: se_sandwich={} expected={expected}",
                diag.se_sandwich[obs]
            );
        }
    }

    #[test]
    fn percentile_index_matches_expected_rounding() {
        assert_eq!(percentile_index(0, 0.95), 0);
        assert_eq!(percentile_index(1, 0.95), 0);
        assert_eq!(percentile_index(10, 0.50), 5);
        assert_eq!(percentile_index(10, 0.95), 9);
    }

    #[test]
    fn percentile_from_sorted_returns_order_statistic() {
        let values = [1.0, 2.0, 3.0, 4.0, 5.0];
        assert_eq!(percentile_from_sorted(&values, 0.50), 3.0);
        assert_eq!(percentile_from_sorted(&values, 0.95), 5.0);
        assert_eq!(percentile_from_sorted(&[], 0.95), 0.0);
    }

    // --- Multi-block ALO tests ---

    use super::{MultiBlockAloInput, compute_multiblock_alo, compute_multiblock_alo_leverages};
    use ndarray::{Array1, Array2};

    #[test]
    fn multiblock_b1_matches_scalar_leverage() {
        // With B=1 the multi-block formula should reduce to the scalar case.
        // H_ii = x_i^T H^{-1} x_i * w_i  (scalar).
        let n = 3;
        let p = 2;
        let x = Array2::from_shape_vec((n, p), vec![1.0, 0.5, 0.8, -0.3, 0.2, 1.1]).unwrap();
        // H = X'WX + I (simple regularisation).
        let w = [1.0, 2.0, 0.5];
        let mut h = Array2::<f64>::eye(p);
        for i in 0..n {
            for r in 0..p {
                for c in 0..p {
                    h[(r, c)] += w[i] * x[(i, r)] * x[(i, c)];
                }
            }
        }
        // Invert H (2x2).
        let det = h[(0, 0)] * h[(1, 1)] - h[(0, 1)] * h[(1, 0)];
        let mut h_inv = Array2::<f64>::zeros((p, p));
        h_inv[(0, 0)] = h[(1, 1)] / det;
        h_inv[(1, 1)] = h[(0, 0)] / det;
        h_inv[(0, 1)] = -h[(0, 1)] / det;
        h_inv[(1, 0)] = -h[(1, 0)] / det;

        // Scalar leverages: a_ii = w_i * x_i^T H^{-1} x_i
        let mut scalar_lev = vec![0.0f64; n];
        for i in 0..n {
            let mut xhx = 0.0;
            for r in 0..p {
                for c in 0..p {
                    xhx += x[(i, r)] * h_inv[(r, c)] * x[(i, c)];
                }
            }
            scalar_lev[i] = w[i] * xhx;
        }

        // Multi-block with B=1.
        let block_designs = vec![x.clone()];
        let block_weights: Vec<Array2<f64>> =
            w.iter().map(|&wi| Array2::from_elem((1, 1), wi)).collect();
        let scores: Vec<Array1<f64>> = (0..n).map(|_| Array1::from_vec(vec![0.1])).collect();
        let eta_hat: Vec<Array1<f64>> = (0..n).map(|i| Array1::from_vec(vec![i as f64])).collect();

        let input = MultiBlockAloInput {
            n_obs: n,
            n_blocks: 1,
            block_designs: &block_designs,
            penalized_hessian_inv: &h_inv,
            block_weights,
            scores,
            eta_hat,
        };

        let result = compute_multiblock_alo(&input).unwrap();
        for i in 0..n {
            assert!(
                (result.leverage[i] - scalar_lev[i]).abs() < 1e-10,
                "leverage mismatch at i={}: got {}, expected {}",
                i,
                result.leverage[i],
                scalar_lev[i]
            );
        }
    }

    #[test]
    fn multiblock_leverage_only_matches_full() {
        // Verify that compute_multiblock_alo_leverages returns the same
        // leverages as compute_multiblock_alo.
        let n = 4;
        let p1 = 2;
        let p2 = 3;
        let x1 = Array2::from_shape_fn((n, p1), |(i, j)| (i + j + 1) as f64 * 0.3);
        let x2 = Array2::from_shape_fn((n, p2), |(i, j)| (i * 2 + j) as f64 * 0.2 - 0.1);
        let p_tot = p1 + p2;
        let h_inv = Array2::<f64>::eye(p_tot); // Simple identity for test.
        let block_weights: Vec<Array2<f64>> = (0..n)
            .map(|i| {
                let v = (i + 1) as f64;
                Array2::from_shape_vec((2, 2), vec![v, 0.1, 0.1, v * 0.5]).unwrap()
            })
            .collect();
        let scores: Vec<Array1<f64>> = (0..n).map(|_| Array1::from_vec(vec![0.0, 0.0])).collect();
        let eta_hat: Vec<Array1<f64>> = (0..n).map(|_| Array1::from_vec(vec![0.0, 0.0])).collect();
        let block_designs = vec![x1.clone(), x2.clone()];

        let input = MultiBlockAloInput {
            n_obs: n,
            n_blocks: 2,
            block_designs: &block_designs,
            penalized_hessian_inv: &h_inv,
            block_weights: block_weights.clone(),
            scores,
            eta_hat,
        };
        let full = compute_multiblock_alo(&input).unwrap();
        let lev_only =
            compute_multiblock_alo_leverages(n, 2, &block_designs, &h_inv, &block_weights).unwrap();

        for i in 0..n {
            assert!(
                (full.leverage[i] - lev_only[i]).abs() < 1e-12,
                "leverage mismatch at i={}: full={}, lev_only={}",
                i,
                full.leverage[i],
                lev_only[i]
            );
        }
    }

    #[test]
    fn multiblock_singular_weight_still_corrects() {
        // When W_i = 0 (singular), the W_i⁻¹-free formula still works:
        // (I - W_i A_i)⁻¹ = I, so Δη = A_i s_i.
        // A_i = x H⁻¹ xᵀ = 1.0² + 0.5² = 1.25 (scalar, B=1).
        let n = 1;
        let p = 2;
        let x = Array2::from_shape_vec((1, p), vec![1.0, 0.5]).unwrap();
        let h_inv = Array2::eye(p);
        let block_designs = vec![x.clone()];
        let block_weights = vec![Array2::from_elem((1, 1), 0.0)]; // singular
        let scores = vec![Array1::from_vec(vec![1.0])];
        let eta_hat = vec![Array1::from_vec(vec![std::f64::consts::PI])];

        let input = MultiBlockAloInput {
            n_obs: n,
            n_blocks: 1,
            block_designs: &block_designs,
            penalized_hessian_inv: &h_inv,
            block_weights,
            scores,
            eta_hat,
        };
        let result = compute_multiblock_alo(&input).unwrap();
        // Δη = A_i * s_i = 1.25 * 1.0 = 1.25
        let expected = std::f64::consts::PI + 1.25;
        assert!(
            (result.eta_tilde[0][0] - expected).abs() < 1e-12,
            "expected {}, got {}",
            expected,
            result.eta_tilde[0][0]
        );
        // Cook's distance should be 0 since W_i = 0.
        assert!(result.cook_distance[0].abs() < 1e-14);
        // ALO variance should be 0 since W_i = 0.
        assert!(result.alo_variance[0][0].abs() < 1e-14);
    }

    #[test]
    fn multiblock_cook_and_variance_basic() {
        // B=1 with known values: verify Cook's distance and variance.
        let n = 1;
        let x = Array2::from_elem((1, 1), 1.0);
        // H⁻¹ = [[0.5]]
        let h_inv = Array2::from_elem((1, 1), 0.5);
        let block_designs = vec![x.clone()];
        let w_val = 2.0;
        let s_val = 0.4;
        let block_weights = vec![Array2::from_elem((1, 1), w_val)];
        let scores = vec![Array1::from_vec(vec![s_val])];
        let eta_hat = vec![Array1::from_vec(vec![1.0])];

        let input = MultiBlockAloInput {
            n_obs: n,
            n_blocks: 1,
            block_designs: &block_designs,
            penalized_hessian_inv: &h_inv,
            block_weights,
            scores,
            eta_hat,
        };
        let result = compute_multiblock_alo(&input).unwrap();

        // A_i = x H⁻¹ xᵀ = 1 * 0.5 * 1 = 0.5
        // (I - W A)⁻¹ = 1 / (1 - 2.0 * 0.5) = 1/0 => regularised
        // Actually 1 - w*a = 1 - 1.0 = 0.0, so det < 1e-12 => regularised with eps=1e-6
        // (I - W A + eps) = 1e-6, so v = s / 1e-6 = 4e5
        // delta_eta = A * v = 0.5 * 4e5 = 2e5
        // This is the regularised case; just check it doesn't panic and returns finite values.
        assert!(result.eta_tilde[0][0].is_finite());
        assert!(result.cook_distance[0].is_finite());
        assert!(result.alo_variance[0][0].is_finite());
    }
}