inlabru: Spatial Inference using Integrated Nested Laplace Approximation

Facilitates spatial modeling using integrated nested Laplace approximation via the INLA package (<http://www.r-inla.org>). 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: 2.1.2
Depends: R (≥ 3.3), sp, stats, methods, ggplot2
Imports: rgdal, rgeos, utils
Suggests: testthat, ggmap, rgl, sphereplot, raster, Matrix, dplyr, maptools, mgcv, shiny, spatstat, spatstat.data, RColorBrewer, graphics, INLA
Published: 2017-11-14
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 gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.inlabru.org,
NeedsCompilation: no
CRAN checks: inlabru results

Downloads:

Reference manual: inlabru.pdf
Package source: inlabru_2.1.2.tar.gz
Windows binaries: r-devel: inlabru_2.1.2.zip, r-release: inlabru_2.1.2.zip, r-oldrel: inlabru_2.1.2.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: not available
Old sources: inlabru archive

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