Convenient model fitting using (iterated) INLA.

## Details

`inlabru`

facilitates Bayesian spatial modelling using integrated nested
Laplace approximations. It is heavily based on R-inla
(https://www.r-inla.org) but adds additional modelling abilities and simplified
syntax for (in particular) spatial models.
Tutorials and more information can be found at
https://inlabru-org.github.io/inlabru/ and http://www.inlabru.org/.
The iterative method used for non-linear predictors is documented in the
`method`

vignette.

The main function for inference using inlabru is `bru()`

. For point process inference `lgcp()`

is
a good starting point.
The general model specification details is documented in `component()`

and `like()`

.
Posterior quantities beyond the basic summaries can be calculated with
a `predict()`

method, documented in `predict.bru()`

.

The package comes with multiple real world data sets, namely gorillas,
mexdolphin, seals. Plotting these data sets is straight forward using inlabru's extensions
to `ggplot2`

, e.g. the `gg()`

function. For educational purposes some simulated data sets are available
as well, e.g. Poisson1_1D, Poisson2_1D, Poisson2_1D and toygroups.

## Author

Fabian E. Bachl bachlfab@gmail.com and Finn Lindgren finn.lindgren@gmail.com