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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
dev = "png",
dev.args = list(type = "cairo-png"),
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# inlabru
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[](https://cran.r-project.org/package=inlabru)
[](https://inlabru-org.r-universe.dev)
[](https://github.com/inlabru-org/inlabru/actions)
[](https://github.com/inlabru-org/inlabru/actions)
[](https://github.com/inlabru-org/inlabru/actions)
[](https://app.codecov.io/gh/inlabru-org/inlabru)
<!-- badges: end -->
The goal of [inlabru](http://inlabru.org) is to facilitate spatial modeling using integrated nested Laplace approximation via the [R-INLA package](https://www.r-inla.org).
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](https://doi.org/10.1111/2041-210X.13168), and `citation("inlabru")`.
The [inlabru.org](http://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/](https://inlabru-org.github.io/inlabru/articles/)
## Installation
You can install the current [CRAN version](https://cran.r-project.org/package=inlabru) version of inlabru:
```{r cran-installation, eval = FALSE}
options(repos = c(
INLA = "https://inla.r-inla-download.org/R/testing",
getOption("repos")
))
install.packages("inlabru")
```
### Installation using [pak](https://pak.r-lib.org/)
You can install the latest bugfix release of inlabru from [GitHub](https://github.com/inlabru-org/inlabru) with:
```{r gh-bugfix-installation-pak, eval = FALSE}
# install.packages("pak")
pak::repo_add(INLA = "https://inla.r-inla-download.org/R/testing")
pak::pkg_install("inlabru-org/inlabru@stable")
```
You can install the development version of inlabru from [GitHub](https://github.com/inlabru-org/inlabru) with
```{r gh-installation-pak, eval = FALSE}
pak::pkg_install("inlabru-org/inlabru")
```
or track the development version builds via [inlabru-org.r-universe.dev](https://inlabru-org.r-universe.dev/builds):
```{r universe-installation-pak, eval = FALSE}
# Enable universe(s) by inlabru-org
pak::repo_add(inlabruorg = "https://inlabru-org.r-universe.dev")
pak::pkg_install("inlabru")
```
This will pick the r-universe version if it is more recent than the CRAN version.
### Installation using `remotes`
You can install the latest bugfix release of inlabru from [GitHub](https://github.com/inlabru-org/inlabru) with:
```{r gh-bugfix-installation-remotes, eval = FALSE}
# install.packages("remotes")
remotes::install_github("inlabru-org/inlabru", ref = "stable")
```
You can install the development version of inlabru from [GitHub](https://github.com/inlabru-org/inlabru) with
```{r gh-installation-remotes, eval = FALSE}
remotes::install_github("inlabru-org/inlabru", ref = "devel")
```
or track the development version builds via [inlabru-org.r-universe.dev](https://inlabru-org.r-universe.dev/builds):
```{r universe-installation-remotes, eval = FALSE}
# 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")
```
## Example
This is a basic example which shows how fit a simple spatial Log Gaussian Cox Process (LGCP)
and predicts its intensity:
```{r example}
# Load libraries
library(INLA)
library(inlabru)
library(fmesher)
library(ggplot2)
# Construct latent model components
matern <- inla.spde2.pcmatern(
gorillas_sf$mesh,
prior.sigma = c(0.1, 0.01),
prior.range = c(0.01, 0.01)
)
cmp <- ~ mySmooth(geometry, model = matern) + Intercept(1)
# Fit LGCP model
# This particular bru/like combination has a shortcut function lgcp() as well
fit <- bru(
cmp,
bru_obs(
formula = geometry ~ .,
family = "cp",
data = gorillas_sf$nests,
samplers = gorillas_sf$boundary,
domain = list(geometry = gorillas_sf$mesh)
),
options = list(control.inla = list(int.strategy = "eb"))
)
# Predict Gorilla nest intensity
lambda <- predict(
fit,
fm_pixels(gorillas_sf$mesh, mask = gorillas_sf$boundary),
~ exp(mySmooth + Intercept)
)
```
```{r plot,fig.cap="Nest intensity per km squared"}
# Plot the result
ggplot() +
geom_fm(data = gorillas_sf$mesh) +
gg(lambda, geom = "tile") +
gg(gorillas_sf$nests, color = "red", size = 0.5, alpha = 0.5) +
ggtitle("Nest intensity per km squared") +
xlab("") +
ylab("")
```