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), .

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.

Installation

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")

Example

This is a basic example which shows you how to solve a common problem:

## basic example code