vglmer: Variational Inference for Hierarchical Generalized Linear Models (original) (raw)
Estimates hierarchical models using variational inference. 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: | 1.0.6 |
|---|---|
| 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: | 2024-11-07 |
| DOI: | 10.32614/CRAN.package.vglmer |
| Author: | Max Goplerud [aut, cre] |
| Maintainer: | Max Goplerud |
| 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 |
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