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> and Goplerud (2024) <doi:10.1017/S0003055423000035>
provide details on the variational algorithms.
| Version: |
1.0.5 |
| 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, gKRLS |
| Published: |
2024-09-12 |
| DOI: |
10.32614/CRAN.package.vglmer |
| Author: |
Max Goplerud [aut, cre] |
| Maintainer: |
Max Goplerud <mgoplerud at austin.utexas.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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