inlabru

The goal of inlabru is to facilitate spatial modeling using integrated nested Laplace approximation via the R-INLA package. Additionally, implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. See Yuan Yuan, Fabian E. Bachl, Finn Lindgren, David L. Borchers, Janine B. Illian, Stephen T. Buckland, Havard Rue, Tim Gerrodette (2017), arXiv.

Installation

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")
remotes::install_github("fbachl/inlabru", ref="stable")

You can install the development version of inlabru from GitHub with:

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

Example

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

if (interactive()) {
# Load libraries
library(inlabru)
library(INLA)
library(ggplot2)

# Load the data
data(gorillas, package = "inlabru")
# If you have PROJ6/GDAL3, you will here need to update the CRS information;
# see separate function below
gorillas <- gorillas_update_CRS(gorillas)

# Construct latent model components
matern <- inla.spde2.pcmatern(gorillas$mesh, 
                              prior.sigma = c(0.1, 0.01), 
                              prior.range = c(5, 0.01))
cmp <- coordinates ~ mySmooth(main = coordinates,
                              model = matern) +
                          Intercept
# Fit LGCP model
fit <- lgcp(cmp,
            data = gorillas$nests,
            samplers = gorillas$boundary,
            domain = list(coordinates = gorillas$mesh),
            options = list(control.inla = list(int.strategy = "eb")))

# Predict Gorilla nest intensity
lambda <- predict(fit, pixels(gorillas$mesh), ~ exp(mySmooth + Intercept))

# Plot the result
ggplot() + 
  gg(lambda) +
  gg(gorillas$nests, color = "red", size = 0.2) +
  coord_equal()
}

If you have an R installation with PROJ6/GDAL3 and INLA >= 20.06.18, you will need to update the CRS information in the gorillas data after reading it in:

gorillas_update_CRS <- function(gorillas) {
  if (inla.has_PROJ6()) {
    gorillas$nests <- rebuild_CRS(gorillas$nests)
    gorillas$boundary <- rebuild_CRS(gorillas$boundary)
    gorillas$mesh$crs <- rebuild_CRS(gorillas$mesh$crs)
    for (name in names(gorillas$gcov)) {
      gorillas$gcov[[name]] <- rebuild_CRS(gorillas$gcov[[name]])
    }
    for (name in names(gorillas$plotsample)) {
      gorillas$plotsample[[name]] <- rebuild_CRS(gorillas$plotsample[[name]])
    }
  }
  gorillas
}