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 how fit a simple spatial Log Gaussian Cox Process (LGCP) and predicts its intensity:

```
# Load libraries
library(INLA)
#> Loading required package: Matrix
#> Loading required package: sp
#> This is INLA_24.06.27 built 2024-06-27 02:36:04 UTC.
#> - See www.r-inla.org/contact-us for how to get help.
#> - List available models/likelihoods/etc with inla.list.models()
#> - Use inla.doc(<NAME>) to access documentation
```

```
library(inlabru)
#> Loading required package: fmesher
```

```
library(fmesher)
library(ggplot2)
# Construct latent model components
<- inla.spde2.pcmatern(
matern $mesh,
gorillas_sfprior.sigma = c(0.1, 0.01),
prior.range = c(0.01, 0.01)
)<- ~ mySmooth(geometry, model = matern) + Intercept(1)
cmp # Fit LGCP model
# This particular bru/like combination has a shortcut function lgcp() as well
<- bru(
fit
cmp,like(
formula = geometry ~ .,
family = "cp",
data = gorillas_sf$nests,
samplers = gorillas_sf$boundary,
domain = list(geometry = gorillas_sf$mesh)
),options = list(control.inla = list(int.strategy = "eb"))
)
# Predict Gorilla nest intensity
<- predict(
lambda
fit,fm_pixels(gorillas_sf$mesh, mask = gorillas_sf$boundary),
~ exp(mySmooth + Intercept)
)
# Plot the result
ggplot() +
geom_fm(data = gorillas_sf$mesh) +
gg(lambda, geom = "tile") +
gg(gorillas$nests, color = "red", size = 0.5, alpha = 0.5) +
ggtitle("Nest intensity per km squared") +
xlab("") +
ylab("")
```