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;
- 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.

Meta-Analyses for Simultaneously Estimating Individual Means
Implement meta-analyses for simultaneously estimating individual means with shrinkage,
isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm.
This methodology is published in Taketomi et al.(2021) <doi:10.3390/axioms10040267>.

Meta-Analysis for Diagnostic Test Studies
Jingyi Guo <jingyi.guo@math.ntnu.no> and Andrea Riebler <andrea.riebler@math.ntnu.no>
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 <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: source("http://www.math.ntnu.no/inla/givemeINLA-testing.R").
R (>= 2.10), RGtk2, cairoDevice, methods, grid

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 <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos="http://www.math.ntnu.no/inla/R/testing").