Package: nimbleQuad 1.4.0

nimbleQuad: Laplace Approximation, Quadrature, and Nested Deterministic Approximation Methods for 'nimble'

Provides deterministic approximation methods for use with the 'nimble' package. These include Laplace approximation and higher-order extension of Laplace approximation using adaptive Gauss-Hermite quadrature (AGHQ), plus nested deterministic approximation methods related to the 'INLA' approach. Additional information is available in the NIMBLE User Manual and a 'nimbleQuad' tutorial, both available at <https://r-nimble.org/documentation.html>.

Authors:Paul van Dam-Bates [aut], Perry de Valpine [aut], Wei Zhang [aut], Christopher Paciorek [aut, cre], Daniel Turek [aut]

nimbleQuad_1.4.0.tar.gz
nimbleQuad_1.4.0.zip(r-4.7-any)nimbleQuad_1.4.0.zip(r-4.6-any)nimbleQuad_1.4.0.zip(r-4.5-any)
nimbleQuad_1.4.0.tgz(r-4.6-any)nimbleQuad_1.4.0.tgz(r-4.5-any)
nimbleQuad_1.4.0.tar.gz(r-4.7-any)nimbleQuad_1.4.0.tar.gz(r-4.6-any)
nimbleQuad_1.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nimbleQuad/json (API)

# Install 'nimbleQuad' in R:
install.packages('nimbleQuad', repos = c('https://paciorek.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.35 score 3 packages 15 scripts 1.6k downloads 25 exports 17 dependencies

Last updated from:f23664a899. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK153
source / vignettesOK179
linux-releaseOK263
macos-releaseOK216
macos-oldrelOK210
windows-develOK104
windows-releaseOK88
windows-oldrelOK113
wasm-releaseOK101

Exports:buildAGHQbuildLaplacebuildNestedApproxcalcMarginalLogLikImprovedconfigureQuadGriddmarginaldrop_algorithmemarginalimproveParamMarginalslogSumExpqmarginalQUAD_RULE_BASEquadGHquadGridCachequadRule_CCDquadRule_GHrmarginalrunAGHQrunLaplacerunNestedApproxsampleLatentssampleParamssetParamGridsummaryAGHQsummaryLaplace

Dependencies:clicodacpp11glueigraphlatticelifecyclemagrittrMatrixnimblenumDerivpkgconfigpolynompracmaR6rlangvctrs

Readme and manuals

Help Manual

Help pageTopics
Main class for nested approximation informationapproxSummary
Laplace approximation and adaptive Gauss-Hermite quadratureAGHQ AGHQuad buildAGHQ buildLaplace Laplace laplace
Build Nested Bayesian Approximation Using Quadrature-based MethodsbuildNestedApprox INLA nested nestedApprox
Calculate improved marginal log-likelihood using grid-based quadraturecalcMarginalLogLikImproved
Configure Quadrature GridsconfigureQuadGrid
Evaluate the marginal posterior density for a parameter.dmarginal
Drop Algorithm to generate permutations of dimension d with a fixed sum.drop_algorithm
Compute the expectation of a function of a parameter under the marginal posterior distributionemarginal
Improve univariate parameter marginals using grid-based quadratureimproveParamMarginals
Log sum exponential.logSumExp
Plot the marginal posterior for a parameterplotMarginal
Compute quantiles for a parameterqmarginal
Base class for nimble function list quadrature rules.QUAD_RULE_BASE
Gauss-Hermite Quadrature Points in one dimensionquadGH
Caching system for building multiple quadrature grids.quadGridCache
Central Composite Design (CCD) used for approximate posterior distributions.quadRule_CCD
Gauss-Hermite Quadrature Rule for Laplace and Approx PosteriorsquadRule_GH
Draw random samples from the marginal posterior of a parameterrmarginal
Combine steps of running Laplace or adaptive Gauss-Hermite quadrature approximationrunAGHQ runLaplace
Run a nested approximation, returning a summary object with default inferencerunNestedApprox
Sample from the posterior distribution of the latent nodessampleLatents
Sample from the parameter posterior distributionsampleParams
Set the parameter grid for the nested approximationsetParamGrid
Summarize results from Laplace or adaptive Gauss-Hermite quadrature approximationsummaryAGHQ summaryLaplace