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().
The general model specification details is documented in bru_component()
and bru_obs().
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(..., bru_obs(model="cp", ...)).
The package comes with multiple real world data sets, namely gorillas,
gorillas_sf, mexdolphin_sf. 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