  This paper introduces the \proglang{R} package \pkg{meta4diag} for
implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our
package \pkg{meta4diag} is a purpose-built front end of the \proglang{R}
package \pkg{INLA}. While \pkg{INLA} offers full Bayesian inference for the
large set of latent Gaussian models using integrated nested Laplace
approximations, \pkg{meta4diag} extracts the features needed for bivariate
meta-analysis and presents them in an intuitive way. It allows the user a
straightforward model-specification and offers user-specific prior
distributions. Further, the newly proposed penalised complexity prior framework
is supported, which builds on prior intuitions about the behaviours of the
variance and correlation parameters. Accurate posterior marginal distributions
for sensitivity and specificity as well as all hyperparameters, and covariates
are directly obtained without Markov chain Monte Carlo sampling. Further,
univariate estimates of interest, such as odds ratios, as well as the SROC
curve and other common graphics are directly available for interpretation. An
interactive graphical user interface provides the user with the full
functionality 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 be
illustrated using three real data examples.
