Convenient model fitting using (iterated) INLA.
Fabian E. Bachl bachlfab@gmail.com and Finn Lindgren finn.lindgren@gmail.com
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().
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().
For point process inference lgcp() can be used as a shortcut to bru(..., like(model="cp", ...)).
The package comes with multiple real world data sets, namely gorillas,
mexdolphin, gorillas_sf, mexdolphin_sf, seals_sp. 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.