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This paper introduces the \proglang{R} package \pkg{meta4diag} forimplementing Bayesian bivariate meta-analyses of diagnostic test studies. Ourpackage \pkg{meta4diag} is a purpose-built front end of the \proglang{R}package \pkg{INLA}. While \pkg{INLA} offers full Bayesian inference for thelarge set of latent Gaussian models using integrated nested Laplaceapproximations, \pkg{meta4diag} extracts the features needed for bivariatemeta-analysis and presents them in an intuitive way. It allows the user astraightforward model-specification and offers user-specific priordistributions. Further, the newly proposed penalised complexity prior frameworkis supported, which builds on prior intuitions about the behaviours of thevariance and correlation parameters. Accurate posterior marginal distributionsfor sensitivity and specificity as well as all hyperparameters, and covariatesare directly obtained without Markov chain Monte Carlo sampling. Further,univariate estimates of interest, such as odds ratios, as well as the SROCcurve and other common graphics are directly available for interpretation. Aninteractive graphical user interface provides the user with the fullfunctionality of the package without requiring any \proglang{R} programming.The package is available through CRAN\url{https://cran.r-project.org/web/packages/meta4diag/} and its usage will beillustrated using three real data examples.

meta4diag: Bayesian Bivariate Meta-analysis of Diagnostic Test Studies for Routine Practice