inlabru: an R package for Bayesian spatial modelling from ecological survey data

Fabian Bachl, Finn Lindgren, David L. Borchers, Janine B. Illian

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

1. Spatial processes are central to many ecological processes, but fitting models that incorporate spatial correlation to data from ecological surveys is computationally challenging. This is particularly true of point pattern data (in which the primary data are the locations at which target species are found), but
also true of gridded data, and of georeferenced samples from continuous spatial fields.
2. We describe here the R package inlabru that builds on the widely-used R-INLA package to provide easier access to Bayesian inference from spatial point process, spatial count, gridded, and georeferenced data, using integrated nested Laplace approximation (INLA, Rue et al., 2009).
3. The package povides methods for fitting spatial density surfaces and estimating abundance, as well as for plotting and prediction. It accommodates data that are points, counts, georeferenced samples, or distance sampling data.
4. This paper describes the main features of the package, illustrated by fitting models to the gorilla nest data contained in the package spatstat (Baddeley & Turner, 2005), a line transect survey data set contained in the package dsm (Miller et al., 2018), and to georeferenced sample from a simulated continuous spatialfield.
Original languageEnglish
Pages (from-to)760-766
Number of pages12
JournalMethods in ecology and evolution
Issue number3
Early online date24 Feb 2019
Publication statusPublished - 3 Jun 2019


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