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 <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: Linking:Please use the canonical form https://CRAN.R-project.org/package=vglmer to link to this page.
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