User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015):
 - common effect and random effects meta-analysis;
 - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
 - three-level meta-analysis model;
 - generalised linear mixed model;
 - logistic regression with penalised likelihood for rare events;
 - Hartung-Knapp method for random effects model;
 - Kenward-Roger method for random effects model;
 - prediction interval;
 - statistical tests  for funnel plot asymmetry;
 - trim-and-fill method to evaluate bias in meta-analysis;
 - meta-regression;
 - cumulative meta-analysis and leave-one-out meta-analysis;
 - import data from 'RevMan 5';
 - produce forest plot summarising several (subgroup) meta-analyses.

Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <https://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).

An interactive application to visualise meta-analysis data as a
    physical weighing machine. The interface is based on the Shiny web application
    framework, though can be run locally and with the user's own data.

Provides common components (classes, methods, documentation) for
  packages that conduct meta-analytic corrections and sensitivity analyses
  for within-study and/or across-study biases in meta-analysis. See the packages
  'PublicationBias', 'phacking', and 'multibiasmeta'. These package implement
  methods described in, respectively: Mathur & VanderWeele (2020)
  <doi:10.31219/osf.io/s9dp6>; Mathur (2022) <doi:10.31219/osf.io/ezjsx>;
  Mathur (2022) <doi:10.31219/osf.io/u7vcb>.