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-4.7-arm64)vglmer_1.0.7.zip(r-4.6-x86_64)vglmer_1.0.7.zip(r-4.6-arm64)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)
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✨
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

4.14 score 21 stars 13 scripts 459 downloads 14 exports 18 dependencies

Last updated from:76c18849b0. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK227
linux-devel-x86_64OK248
source / vignettesOK200
linux-release-arm64OK205
linux-release-x86_64OK255
macos-release-arm64OK177
macos-release-x86_64OK310
macos-oldrel-arm64OK150
macos-oldrel-x86_64OK381
windows-devel-arm64OK323
windows-devel-x86_64OK193
windows-release-arm64OK312
windows-release-x86_64OK206
windows-oldrel-x86_64OK228
wasm-releaseOK126

Exports:add_formula_SLELBOfixefformat_glmerformat_vglmerMAVBposterior_samples.vglmerpredict_MAVBranefSL.glmerSL.vglmerv_svglmervglmer_control

Dependencies:bootCholWishartlatticelme4lmtestMASSMatrixmgcvminqamvtnormnlmenloptrrbibutilsRcppRcppEigenRdpackreformulaszoo

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