Struct MarginalBlockJacobian 

pub struct MarginalBlockJacobian { /* private fields */ }

n_outputs=3 stacked Jacobian for the marginal block.

The marginal block contributes identically to q0 and q1 (both entry and exit probit arguments) but not to ad1 (the derivative). The stacked Jacobian is:

∂η0[i]/∂β = c[i] · M[i,:]
∂η1[i]/∂β = c[i] · M[i,:]
∂ad1[i]/∂β = 0

At g=0 (β=0 init): c=1, so each row is just M[i,:].

Implementations

impl MarginalBlockJacobian

pub fn new(design: impl Into<Arc<Array2<f64>>>) -> Self

Trait Implementations

impl BlockEffectiveJacobian for MarginalBlockJacobian

fn effective_jacobian_rows( &self, state: &FamilyLinearizationState<'_>, rows: Range<usize>, ) -> Result<Array2<f64>, String>

Stacked multi-output Jacobian for a contiguous observation row range.

fn n_outputs(&self) -> usize

Number of stacked output channels. 1 for most blocks.

fn effective_jacobian_at( &self, state: &FamilyLinearizationState<'_>, ) -> Result<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, String>

Stacked multi-output Jacobian at the current β.

fn eta_row_scaling_for_skewness(&self) -> Option<Arc<[f64]>>

Returns the per-row scaling vector when this callback is a simple diagonal-scaling block (RowScaledJacobian). Used by the identifiability audit’s skewness-aware bias correction (T25).