  The INLA package provides a tool for computationally efficient Bayesian
modeling and inference for various widely used models, more formally the class
of latent Gaussian models. It is a non-sampling based framework which provides
approximate results for Bayesian inference, using sparse matrices. The swift
uptake of this framework for Bayesian modeling is rooted in the computational
efficiency of the approach and catalyzed by the demand presented by the big
data era. In this paper, we present new developments within the INLA package
with the aim to provide a computationally efficient mechanism for the Bayesian
inference of relevant challenging situations.
