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.

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.
 For the pretest estimator, the confidence interval for individual means are provided.
 Methodologies were published in 
 Taketomi et al. (2021) <doi:10.3390/axioms10040267>, Taketomi et al. (2022) <doi:10.3390/a15010026>, 
 Taketomi et al. (2023-) (under review).

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.