dot-get_B_gee_glm_woodbury: Path 1b-Woodbury: Non-Gaussian GEE with Woodbury Acceleration

In lgspline: Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation

Path 1b-Woodbury: Non-Gaussian GEE with Woodbury Acceleration

Description

Non-Gaussian correlated solve using the Woodbury factorization. At each damped Newton step, the current weighted information matrix is split into a block-diagonal part defining \mathbf{G}_{\mathrm{on}} and a low-rank cross-partition part. The constrained step is then taken through .lagrangian_project_woodbury().

Usage

.get_B_gee_glm_woodbury(
  X,
  y,
  K,
  p_expansions,
  VhalfInv_perm,
  order_list,
  A,
  R_constraints,
  constraint_value_vectors,
  family,
  return_G_getB,
  iterate,
  tol,
  quadprog,
  qp_Amat,
  qp_bvec,
  qp_meq,
  qp_score_function,
  observation_weights,
  Lambda,
  Lambda_block,
  unique_penalty_per_partition,
  L_partition_list,
  wb_decomp_init,
  wb_sqrt_init,
  glm_weight_function,
  schur_correction_function,
  need_dispersion_for_estimation,
  dispersion_function,
  VhalfInv,
  parallel_eigen,
  parallel_aga,
  parallel_matmult,
  cl,
  chunk_size,
  num_chunks,
  rem_chunks,
  parallel_qr = FALSE,
  initial_active_ineq = integer(0),
  ...
)

Arguments

X, y	Lists of partition-specific design matrices and responses.
K, p_expansions	Integer dimensions.
VhalfInv_perm	\mathbf{V}^{-1/2} permuted to partition ordering.
order_list, observation_weights	Standard partition arguments.
A	Constraint matrix (P \times R).
constraint_value_vectors	Constraint RHS list encoding \mathbf{A}^{\top}\boldsymbol{\beta} = \mathbf{c}.
family	GLM family object.
return_G_getB	Logical; return covariance components.
iterate	Logical; if TRUE, iterate until convergence.
tol	Convergence tolerance.
qp_Amat, qp_bvec, qp_meq	QP constraint specification.
qp_score_function	Score function for QP step.
Lambda, Lambda_block	Shared and full penalty matrices.
unique_penalty_per_partition	Logical.
L_partition_list	Partition-specific penalty matrices.
glm_weight_function	Function computing GLM working weights.
schur_correction_function	Function computing Schur corrections.
need_dispersion_for_estimation	Logical.
dispersion_function	Dispersion estimation function.
VhalfInv	Inverse square root correlation matrix.
parallel_eigen, parallel_aga, parallel_matmult	Logical flags.
cl, chunk_size, num_chunks, rem_chunks	Parallel parameters.
...	Passed to sub-functions.

Value

Same structure as .get_B_gee_glm.

lgspline documentation built on Aug. 5, 2026, 1:10 a.m.