The goal of inlabru is to facilitate
spatial modeling using integrated nested Laplace approximation via the
R-INLA package. Additionally,
extends the GAM-like model class to more general nonlinear predictor
expressions, and implements a log Gaussian Cox process likelihood for
modeling univariate and spatial point processes based on ecological
survey data. Model components are specified with general inputs and
mapping methods to the latent variables, and the predictors are
specified via general R expressions, with separate expressions for each
observation likelihood model in multi-likelihood models. A prediction
method based on fast Monte Carlo sampling allows posterior prediction of
general expressions of the latent variables. See Fabian E. Bachl, Finn
Lindgren, David L. Borchers, and Janine B. Illian (2019), inlabru: an R
package for Bayesian spatial modelling from ecological survey data,
Methods in Ecology and Evolution, British Ecological Society, 10,
760–766, doi:10.1111/2041-210X.13168,
and `citation("inlabru")`

.

The inlabru.org website has links to old tutorials with code examples for versions up to 2.1.13. For later versions, updated versions of these tutorials, as well as new examples, can be found at https://inlabru-org.github.io/inlabru/articles/

You can install the current CRAN version of inlabru:

`install.packages("inlabru")`

You can install the latest bugfix release of inlabru from GitHub with:

```
# install.packages("remotes")
::install_github("inlabru-org/inlabru", ref = "stable") remotes
```

You can install the development version of inlabru from GitHub with

```
# install.packages("remotes")
::install_github("inlabru-org/inlabru", ref = "devel") remotes
```

or track the development version builds via inlabru-org.r-universe.dev:

```
# Enable universe(s) by inlabru-org
options(repos = c(
inlabruorg = "https://inlabru-org.r-universe.dev",
INLA = "https://inla.r-inla-download.org/R/testing",
CRAN = "https://cloud.r-project.org"
))
# Install some packages
install.packages("inlabru")
```

This is a basic example which shows you how fit a simple spatial Log Gaussian Cox Process (LGCP) and predicts its intensity:

```
# Load libraries
library(inlabru)
#> Loading required package: sp
library(INLA)
#> Loading required package: Matrix
#> Loading required package: foreach
#> Loading required package: parallel
#> This is INLA_22.11.28-1 built 2022-11-28 08:04:58 UTC.
#> - See www.r-inla.org/contact-us for how to get help.
#> - To enable PARDISO sparse library; see inla.pardiso()
library(ggplot2)
# Load the data
data(gorillas, package = "inlabru")
# Construct latent model components
<- inla.spde2.pcmatern(gorillas$mesh,
matern prior.sigma = c(0.1, 0.01),
prior.range = c(0.01, 0.01)
)<- coordinates ~ mySmooth(coordinates, model = matern) + Intercept(1)
cmp # Fit LGCP model
# This particular bru/like combination has a shortcut function lgcp() as well
<- bru(
fit components = cmp,
like(
formula = coordinates ~ .,
family = "cp",
data = gorillas$nests,
samplers = gorillas$boundary,
domain = list(coordinates = gorillas$mesh)
),options = list(control.inla = list(int.strategy = "eb"))
)
# Predict Gorilla nest intensity
<- predict(
lambda
fit,pixels(gorillas$mesh, mask = gorillas$boundary),
~ exp(mySmooth + Intercept)
)
# Plot the result
ggplot() +
gg(lambda) +
gg(gorillas$nests, color = "red", size = 0.2) +
coord_equal() +
ggtitle("Nest intensity per km squared")
```

If you
have an R installation with PROJ6/GDAL3, and INLA >= 20.06.18, and
loading old spatial objects, you may need to apply the
`rgdal::rebuild_CRS()`

method on them before they are fully
usable. The data objects in `inlabru`

have been updated, so
should not need this conversion anymore.