  The integrated nested Laplace approximation (INLA) method has become a
popular approach for computationally efficient approximate Bayesian
computation. In particular, by leveraging sparsity in random effect precision
matrices, INLA is commonly used in spatial and spatio-temporal applications.
However, the speed of INLA comes at the cost of restricting the user to the
family of latent Gaussian models and the likelihoods currently implemented in
{INLA}, the main software implementation of the INLA methodology.
  {inlabru} is a software package that extends the types of models that can be
fitted using INLA by allowing the latent predictor to be non-linear in its
parameters, moving beyond the additive linear predictor framework to allow more
complex functional relationships. For inference it uses an approximate
iterative method based on the first-order Taylor expansion of the non-linear
predictor, fitting the model using INLA for each linearised model
configuration.
  {inlabru} automates much of the workflow required to fit models using
{R-INLA}, simplifying the process for users to specify, fit and predict from
models. There is additional support for fitting joint likelihood models by
building each likelihood individually. {inlabru} also supports the direct use
of spatial data structures, such as those implemented in the {sf} and {terra}
packages.
  In this paper we outline the statistical theory, model structure and basic
syntax required for users to understand and develop their own models using
{inlabru}. We evaluate the approximate inference method using a Bayesian method
checking approach. We provide three examples modelling simulated spatial data
that demonstrate the benefits of the additional flexibility provided by
{inlabru}.
