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.

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>.

The sample mean and standard deviation are two commonly used statistics in meta-analyses, 
    but some trials use other summary statistics such as the median and quartiles to report the results. 
    Therefore, researchers need to transform those information back to the sample mean and 
    standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.