rSPDE is an R package used for computing rational approximations of fractional SPDEs. These rational approximations can be used for computatially efficient statistical inference.
Basic statistical operations such as likelihood evaluations and kriging predictions using the fractional approximations are also implemented. The package also contains an interface to R-INLA.
For illustration purposes, the package contains a simple FEM implementation for models on R. See the
Getting Started to the rSPDE package vignette for an introduction to the package. The Rational approximation with the rSPDE package and Operator-based rational approximation vignettes provide
introductions to how to create and fit rSPDE models. For an introduction to the R-INLA implementation
of the rSPDE models see the R-INLA implementation of the rational SPDE approach. The rSPDE documentation contains descriptions and examples of the functions in the rSPDE package.
D. Bolin and K. Kirchner (2020) The rational SPDE approach for Gaussian random fields with general smoothness. Journal of Computational and Graphical Statistics, 29:2, 274-285.
The latest CRAN release of the package can be installed directly from CRAN with install.packages("rSPDE").
The latest stable version (which is sometimes slightly more recent than the CRAN version), can be installed by using the command
remotes::install_github("davidbolin/rspde", ref = "stable")in R. The development version can be installed using the command
remotes::install_github("davidbolin/rspde", ref = "devel")If you want to install the package using the remotes::install_github-method on Windows, you first need to install Rtools and add the paths to Rtools and gcc to the Windows PATH environment variable. This can be done for the current R session only using the commands
rtools = "C:\\Rtools\\bin"
gcc = "C:\\Rtools\\gcc-4.6.3\\bin"
Sys.setenv(PATH = paste(c(gcc, rtools, Sys.getenv("PATH")), collapse = ";"))where the variables rtools and gcc need to be changed if Rtools is not installed directly on C:.
- Implementation of covariance-based non-stationary models.
- Implementation of the covariance-based rational approximation on R-STAN interface.
- Implementation of covariance-based method to more general SPDE models.
- Implementation of PC-priors for R-INLA
rSPDEmodels.
The package version format for released versions is major.minor.bugfix. All regular development should be performed on the devel branch or in a feature branch, managed with git flow feature. On the devel branch, the vestion number is major.minor.bugfix.9000, where the first three components reflect the latest released version with changes present in the default branch. Bugfixes should be applied via the git flow bugfix and git flow hotfix methods, as indicated below. For git flow configuration, use master as the stable master branch, devel as the develop branch, and v as the version tag prefix. See the git flow tutorial for more information.
For non master and devel branches that collaborators need access to (e.g. release branches, feature branches, etc, use the git flow publish mechanism).
- Prepare a new stable release with CRAN submission:
git flow release start major.(minor+1).0
usethis::use_version("minor") # In R (updates the version number in DESCRIPTION and NEWS)
## At this point, see the CRAN submission section below.
git flow release finish 'VERSION'
usethis::use_dev_version() # In R (updates the dev version number in DESCRIPTION and NEWS)
- Do a hotfix (branch from stable branch; use bugfix for release branch bugfixes):
git flow hotfix start hotfix_branch_name
## Do the bugfix, update the verison number major.minor.(bugfix+1), and commit
## Optionally, do CRAN submission
git flow hotfix finish hotfix_branch_name
## Resolve merge conflicts, if any
- CRAN submission
## Perform CRAN checks (usually on the release branch version)
## If unsuccessful then do bugfixes with increasing bugfix version, until ok
## Submit to CRAN
## If not accepted then do more bugfixes and repeat