inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the 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. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Version: 2.3.1
Depends: ggplot2, methods, R (≥ 3.5), sp (≥ 1.4-5), stats
Imports: Matrix, rgdal (≥ 1.5.8), rgeos, utils, withr
Suggests: covr, dplyr, ggmap, ggpolypath, graphics, INLA (≥ 20.06.18), knitr, maptools, mgcv, patchwork, raster, RColorBrewer, rgl, rmarkdown, shiny, sn, spatstat.geom, spatstat.core, spatstat.data, spatstat (≥ 2.0-0), sphereplot, testthat
Published: 2021-03-22
Author: Finn Lindgren ORCID iD [aut, cre, cph] (Finn Lindgren continued development of the main code), Fabian E. Bachl [aut, cph] (Fabian Bachl wrote the main code), David L. Borchers [ctb, dtc, cph] (David Borchers wrote code for Gorilla data import and sampling, multiplot tool), Daniel Simpson [ctb, cph] (Daniel Simpson wrote the basic LGCP sampling method), Lindesay Scott-Howard [ctb, dtc, cph] (Lindesay Scott-Howard provided MRSea data import code), Seaton Andy [ctb] (Andy Seaton provided testing and bugfixes)
Maintainer: Finn Lindgren <finn.lindgren at gmail.com>
BugReports: https://github.com/inlabru-org/inlabru/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.inlabru.org, https://inlabru-org.github.io/inlabru/
NeedsCompilation: no
Additional_repositories: https://inla.r-inla-download.org/R/testing
Citation: inlabru citation info
Materials: README NEWS
CRAN checks: inlabru results

Documentation:

Reference manual: inlabru.pdf
Vignettes: Nonlinear model approximation
Iterative INLA method

Downloads:

Package source: inlabru_2.3.1.tar.gz
Windows binaries: r-devel: inlabru_2.3.1.zip, r-release: inlabru_2.3.1.zip, r-oldrel: inlabru_2.3.1.zip
macOS binaries: r-release (arm64): inlabru_2.3.1.tgz, r-release (x86_64): inlabru_2.3.1.tgz, r-oldrel: inlabru_2.3.1.tgz
Old sources: inlabru archive

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