This page hosts a supplement package to the paper 'Multivariate Spatial Covariance Models: A Conditional Approach' by Cressie and Zammit-Mangion (2017, Biometrika, in press). This package can be used for the construction of covariance matrices for bivariate processes modelled using the conditional approach. The covariance functions are assumed to be Matern covariance functions whilst functionality is provided for constructing interaction matrices using the bisquare function as the interaction function. The accompanying vignettes may be used to replicate the studies found in the paper.
To download the vignettes without reproducing them on your machine, please view them in the vignettes folder or by directly click on the following links:
If you wish to reproduce the results, you will need to install the package and its dependencies. Two of the dependencies, INLA and gpclib need to be installed manually. For INLA please visit the INLA project page for installation instructions. For gpclib please type
install.packages("gpclib", type = "source")
For vignette compilation please make sure you also have a LaTeX distribution installed.
To install the bicon package and the other dependencies, please install devtools and then type
library(devtools)
install_github("andrewzm/bicon",build_vignettes=T,dependencies=T)
If you do not wish to compile the vignettes (which takes a while) please set build_vignettes=F above. Otherwise, to view the vignettes please type
library(bicon)
vignette()
and select the vignettes under bicon.
Cressie, N., & Zammit-Mangion, A. (2017). Multivariate spatial covariance models: A conditional approach. Biometrika, in press.