# Laplace Approximation, Quadrature, and Nested Deterministic Approximation Methods for 'nimble'

## Documentation for package ‘nimbleQuad’ version 1.4.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

AGHQ | Laplace approximation and adaptive Gauss-Hermite quadrature
AGHQuad | Laplace approximation and adaptive Gauss-Hermite quadrature
approxSummary | Main class for nested approximation information
buildAGHQ | Laplace approximation and adaptive Gauss-Hermite quadrature
buildLaplace | Laplace approximation and adaptive Gauss-Hermite quadrature
buildNestedApprox | Build Nested Bayesian Approximation Using Quadrature-based Methods
calcMarginalLogLikImproved | Calculate improved marginal log-likelihood using grid-based quadrature
configureQuadGrid | Configure Quadrature Grids
dmarginal | Evaluate the marginal posterior density for a parameter.
drop_algorithm | Drop Algorithm to generate permutations of dimension d with a fixed sum.
emarginal | Compute the expectation of a function of a parameter under the marginal posterior distribution
improveParamMarginals | Improve univariate parameter marginals using grid-based quadrature
INLA | Build Nested Bayesian Approximation Using Quadrature-based Methods
Laplace | Laplace approximation and adaptive Gauss-Hermite quadrature
laplace | Laplace approximation and adaptive Gauss-Hermite quadrature
logSumExp | Log sum exponential.
nested | Build Nested Bayesian Approximation Using Quadrature-based Methods
nestedApprox | Build Nested Bayesian Approximation Using Quadrature-based Methods
plotMarginal | Plot the marginal posterior for a parameter
qmarginal | Compute quantiles for a parameter
quadGH | Gauss-Hermite Quadrature Points in one dimension
quadGridCache | Caching system for building multiple quadrature grids.
quadRule_CCD | Central Composite Design (CCD) used for approximate posterior distributions.
quadRule_GH | Gauss-Hermite Quadrature Rule for Laplace and Approx Posteriors
QUAD_RULE_BASE | Base class for nimble function list quadrature rules.
rmarginal | Draw random samples from the marginal posterior of a parameter
runAGHQ | Combine steps of running Laplace or adaptive Gauss-Hermite quadrature approximation
runLaplace | Combine steps of running Laplace or adaptive Gauss-Hermite quadrature approximation
runNestedApprox | Run a nested approximation, returning a summary object with default inference
sampleLatents | Sample from the posterior distribution of the latent nodes
sampleParams | Sample from the parameter posterior distribution
setParamGrid | Set the parameter grid for the nested approximation
summaryAGHQ | Summarize results from Laplace or adaptive Gauss-Hermite quadrature approximation
summaryLaplace | Summarize results from Laplace or adaptive Gauss-Hermite quadrature approximation