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 |
| DOI: |
10.32614/CRAN.package.vglmer |
| 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:
Downloads:
Reverse dependencies:
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