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)
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:
You can install the latest bugfix release of inlabru from GitHub with:
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)
NEWS.md file to track changes to the package.
inla methods for
generate() that convert
inla output into
bru objects before calling the
and posterior sample generator.
Added protection for examples requiring optional packages
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 effects
Added matrix geom
Fixed CRAN test issues