Changes in version 1.4.0 (2026-01-14)                  

These change notes reflect changes in functionality relative to the
implementation of Laplace/AGHQ approximation in the nimble package,
version 1.3.0.

USER LEVEL CHANGES

  - Move Laplace/AGHQ approximation from nimble to nimbleQuad package.

  - Add a nested approximation method that provides approximate
    posterior inference using methodology similar to the well-known INLA
    approach (implemented in the R-INLA package) and to the related
    methods for extended Gaussian latent models (EGLMs) of Stringer et
    al. (2023), implemented in the aghq R package.

  - Greatly improve efficiency of Laplace/AGHQ approximation through
    major revision of derivative calculations, including removal of
    "computeMethod" options (PR 52).

  - Greatly improve efficiency of Laplace/AGHQ approximation when using
    the multivariate normal distribution, via a new dmnormAD version of
    the distribution customized for efficient computation with NIMBLE's
    AD system.

  - Greatly improve efficiency of building Laplace/AGHQ when determining
    parameter dependencies in setup_OneAGHQuad (PR 48).

  - Greatly improve efficiency of Laplace/AGHQ summary computation of
    Hessian.

  - Automatically make use of analytic gradient and Hessian with respect
    to the random variables for univariate and multivariate normal
    distributions rather than AD in Laplace/AGHQ approximation. This
    also causes the "outer" Laplace/AGHQ optimization to not use the
    AD-based gradient.

  - Greatly improve the efficiency of building overall Laplace
    approximation when using many individual Laplace approximations (PR
    #59).

  - Inform user when overall Laplace approximation is decomposed into
    multiple Laplace approximations.

  - Improve messaging when Laplace/AGHQ approximation does not converge.

  - Provide a variety of quadrature grids, extending from standard AGHQ,
    including grid pruning, sparse grids, and allowing user-provided
    grids.

  - Automatically remove random effect nodes with no dependent data from
    Laplace (Issue #56).

  - Add ability to find posterior mode (as well as prior-penalized MLE,
    as used in fisheries) via Laplace approximation.

BUG FIXES

  - Fix handling of fnscale when updateSettings is used.

  - Fix buildAGHQ for case with no random effects.

  - Fix length of latent nodes in Laplace/AGHQ under non-1:1
    transformation (PR #41).

  - Fix seg fault when "model" option passed as innerOptimStart (Issue
    #62).

DEVELOPER LEVEL CHANGES

  - Completely refactor and generalize the quadrature grids
    implementation, including making consistent with mvQuad.