vglmer: Variational Inference for Hierarchical Generalized Linear Models

Estimates hierarchical models using mean-field variational Bayes. At present, it can estimate logistic, linear, and negative binomial models. It can accommodate models with an arbitrary number of random effects and requires no integration to estimate. It also provides the ability to improve the quality of the approximation using marginal augmentation. Goplerud (2022) <doi:10.1214/21-BA1266> provides details on the variational algorithms.

Version:	1.0.3
Depends:	R (≥ 3.0.2)
Imports:	Rcpp (≥ 1.0.1), lme4, CholWishart, mvtnorm, Matrix, stats, graphics, methods, lmtest, splines, mgcv
LinkingTo:	Rcpp, RcppEigen (≥ 0.3.3.4.0)
Suggests:	SuperLearner, MASS, tictoc, testthat
Published:	2022-10-27
Author:	Max Goplerud [aut, cre]
Maintainer:	Max Goplerud <mgoplerud at pitt.edu>
BugReports:	https://github.com/mgoplerud/vglmer/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/mgoplerud/vglmer
NeedsCompilation:	yes
Materials:	README NEWS
In views:	Bayesian, MixedModels
CRAN checks:	vglmer results

Documentation:

Reference manual:

vglmer.pdf

Downloads:

Package source:	vglmer_1.0.3.tar.gz
Windows binaries:	r-devel: vglmer_1.0.3.zip, r-release: vglmer_1.0.3.zip, r-oldrel: vglmer_1.0.3.zip
macOS binaries:	r-release (arm64): vglmer_1.0.3.tgz, r-oldrel (arm64): vglmer_1.0.3.tgz, r-release (x86_64): vglmer_1.0.3.tgz, r-oldrel (x86_64): vglmer_1.0.3.tgz
Old sources:	vglmer archive

Reverse dependencies:

Reverse imports:

autoMrP

Linking:

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