inlabru: Spatial Inference using Integrated Nested Laplace Approximation

Facilitates spatial modeling using integrated nested Laplace approximation via the 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:1604.06013>.

Version: 2.1.3
Depends: R (≥ 3.3), sp, stats, methods, ggplot2
Imports: rgdal, rgeos, utils
Suggests: testthat, ggmap, rgl, sphereplot, raster, Matrix, dplyr, maptools, mgcv, shiny, spatstat,, RColorBrewer, graphics, INLA
Published: 2018-02-11
Author: Fabian E. Bachl (main code), Finn Lindgren (SPDE posterior plotting), David L. Borchers (Gorilla data import and sampling, multiplot tool), Daniel Simpson (basic LGCP sampling method), Lindesay Scott-Hayward (MRSea data import)
Maintainer: Fabian E. Bachl <bachlfab at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: inlabru results


Reference manual: inlabru.pdf
Package source: inlabru_2.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: inlabru_2.1.3.tgz
OS X Mavericks binaries: r-oldrel: not available
Old sources: inlabru archive


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