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),
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.
You can install the current CRAN version of inlabru:
install.packages("inlabru")
You can install the latest bugfix release of inlabru from GitHub with:
devtools::install_github("fbachl/inlabru", ref="master")
You can install the development version of inlabru from GitHub with:
# install.packages("devtools")devtools::install_github("fbachl/inlabru", ref="devel")
This is a basic example which shows you how to solve a common problem:
## basic example code
Update default options
Prevent int.polygon from integrating outside the mesh domain, and generally more robust integration scheme construction.
Fix bru() to like() parameter logic. (Thanks to Peter Vesk for bug example)
Added a NEWS.md
file to track changes to the package.
Added inla
methods for predict()
and generate()
that convert
inla
output into bru
objects before calling the bru
prediction
and posterior sample generator.
Added protection for examples requiring optional packages
Fix sample.lgcp
output formatting, extended CRS support, and more efficient sampling algorithm
Avoid dense matrices for effect mapping
iinla()
tracks convergence of both fixed and random effectsAdded matrix geom gg.matrix()
Fixed CRAN test issues