Package: metaGLMM 1.0.0

metaGLMM: Aggregate-Data Meta-Analysis with Generalized Linear Mixed Models

Extends traditional random-effects meta-analysis by embedding it within a generalized linear mixed-effects model framework. The package supports covariate adjustment and non-normal responses using aggregate data, and provides likelihood-based inference with computationally efficient likelihood evaluation. The underlying methodology is described in Hanada and Sugimoto (2026) <doi:10.1093/biomtc/ujag148>.

Authors:Keisuke Hanada [aut, cre]

metaGLMM_1.0.0.tar.gz
metaGLMM_1.0.0.zip(r-4.7-x86_64)metaGLMM_1.0.0.zip(r-4.7-arm64)metaGLMM_1.0.0.zip(r-4.6-x86_64)metaGLMM_1.0.0.zip(r-4.6-arm64)metaGLMM_1.0.0.zip(r-4.5-x86_64)
metaGLMM_1.0.0.tgz(r-4.6-x86_64)metaGLMM_1.0.0.tgz(r-4.6-arm64)metaGLMM_1.0.0.tgz(r-4.5-x86_64)metaGLMM_1.0.0.tgz(r-4.5-arm64)
metaGLMM_1.0.0.tar.gz(r-4.7-arm64)metaGLMM_1.0.0.tar.gz(r-4.7-x86_64)metaGLMM_1.0.0.tar.gz(r-4.6-arm64)metaGLMM_1.0.0.tar.gz(r-4.6-x86_64)
metaGLMM_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
metaGLMM/json (API)

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

Bug tracker:https://github.com/keisuke-hanada/metaglmm/issues

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

On CRAN:

Conda:

cpp

5.89 score 2 stars 9 scripts 374 downloads 15 exports 10 dependencies

Last updated from:00a5f89a40. Checks:15 OK. Indexed: yes.

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linux-devel-x86_64OK121
source / vignettesOK241
linux-release-arm64OK154
linux-release-x86_64OK124
macos-release-arm64OK187
macos-release-x86_64OK430
macos-oldrel-arm64OK195
macos-oldrel-x86_64OK339
windows-devel-arm64OK346
windows-devel-x86_64OK173
windows-release-arm64OK354
windows-release-x86_64OK92
windows-oldrel-x86_64OK133
wasm-releaseOK93

Exports:as_metafor_dataci_metaGLMMconfint_ANconfint_GSBCconfint_PLconfint_SBCforestlikelihoodmake_ll_funmake_ll_fun.fastmetaGLMMmetaGLMM_familymle2_fitprofile_llprofile_ll_sbc

Dependencies:bbmlebdsmatrixfastGHQuadlatticeMASSMatrixmvtnormnumDerivqrngRcpp

Getting started with metaGLMM
Overview | A small Gaussian analysis | A positive-response example | Where to go next
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-08-22
Started: 2026-08-21

Custom families
The family contract | Fit an overdispersed rate model with QMC | Validation and limitations
Rendered fromcustom-family.Rmdusingknitr::rmarkdown

Last update: 2026-08-22
Started: 2026-08-21

Gaussian aggregate outcomes
Weighted study estimates | Basic random-effects analysis and forest plot | Fixed-effect reference | Estimating heterogeneity | Formula and contrasts
Rendered fromgaussian.Rmdusingknitr::rmarkdown

Last update: 2026-08-22
Started: 2026-08-21

Binary aggregate outcomes
Data contract | Fit the grouped model | Study-level contrasts and a forest plot
Rendered frombinary.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2026-08-21

Poisson rate outcomes
Rates from aggregate counts | Basic single-group meta-analysis and forest plot | Meta-regression with moderators | Offsets
Rendered frompoisson.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2026-08-21

Prediction and forest plots
A fitted model | Prediction for a future study | Forest plots | Plain data exchange
Rendered fromprediction-forest.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2026-08-21

Likelihood-based inference
Fit a model | Wald intervals | Profile and SBC intervals | Inspecting the likelihood fit
Rendered frominference.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2026-08-21