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

# 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 670 downloads 14 exports 19 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK250
linux-devel-x86_64OK237
source / vignettesOK187
linux-release-arm64OK230
linux-release-x86_64OK231
macos-release-arm64OK205
macos-release-x86_64OK346
macos-oldrel-arm64OK134
macos-oldrel-x86_64OK303
windows-develOK260
windows-releaseOK253
windows-oldrelOK247
wasm-releaseOK135

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