Package: vglmer 1.0.7

vglmer: Variational Inference for Hierarchical Generalized Linear Models

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.

Authors:Max Goplerud [aut, cre]

vglmer_1.0.7.tar.gz
vglmer_1.0.7.zip(r-4.7-x86_64)vglmer_1.0.7.zip(r-4.6-x86_64)vglmer_1.0.7.zip(r-4.5-x86_64)
vglmer_1.0.7.tgz(r-4.6-x86_64)vglmer_1.0.7.tgz(r-4.6-arm64)vglmer_1.0.7.tgz(r-4.5-x86_64)vglmer_1.0.7.tgz(r-4.5-arm64)
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vglmer_1.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
vglmer/json (API)

# Install 'vglmer' in R:
install.packages('vglmer', repos = c('https://mgoplerud.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mgoplerud/vglmer/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

4.32 score 21 stars 9 scripts 736 downloads 14 exports 19 dependencies

Last updated from:76c18849b0. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK214
linux-devel-x86_64OK235
source / vignettesOK213
linux-release-arm64OK219
linux-release-x86_64OK238
macos-release-arm64OK177
macos-release-x86_64OK358
macos-oldrel-arm64OK197
macos-oldrel-x86_64OK322
windows-develOK239
windows-releaseOK205
windows-oldrelOK214
wasm-releaseOK180

Exports:add_formula_SLELBOfixefformat_glmerformat_vglmerMAVBposterior_samples.vglmerpredict_MAVBranefSL.glmerSL.vglmerv_svglmervglmer_control

Dependencies:bootCholWishartlatticelme4lmtestMASSMatrixmgcvminqamvtnormnlmenloptrrbibutilsRcppRcppEigenRdpackreformulasrlangzoo

Readme and manuals

Help Manual

Help pageTopics
Perform MAVB after fitting vglmerMAVB
Draw samples from the variational distributionposterior_samples.vglmer
SuperLearner with (Variational) Hierarchical Modelsadd_formula_SL predict.SL.glmer predict.SL.vglmer SL.glmer SL.vglmer sl_vglmer
Create splines for use in vglmerv_s
Variational Inference for Hierarchical Generalized Linear Modelsvglmer
Control for vglmer estimationvglmer_control
Predict after vglmerpredict.vglmer predict_MAVB vglmer_predict
Generic Functions after Running vglmercoef.vglmer ELBO fitted.vglmer fixef.vglmer format_glmer format_vglmer formula.vglmer print.vglmer ranef.vglmer sigma.vglmer summary.vglmer vcov.vglmer vglmer-class