  The INLA approach for approximate Bayesian inference for latent Gaussian
models has been shown to give fast and accurate estimates of posterior
marginals and also to be a valuable tool in practice via the R-package R-INLA.
In this paper we formalize new developments in the R-INLA package and show how
these features greatly extend the scope of models that can be analyzed by this
interface. We also discuss the current default method in R-INLA to approximate
posterior marginals of the hyperparameters using only a modest number of
evaluations of the joint posterior distribution of the hyperparameters, without
any need for numerical integration.
