Fast Gaussian-process inference using general Vecchia approximations
For examples of how to use the package, please see the vignettes folder. Please note that GPvecchia is under active development and not stable at this time.
If you have an issue with GPvecchia, including unsatisfactory runtime, please open a Github ticket.
<!---To ensure that the algorithms run efficiently on your computer, we recommend installing the package by downloading the repo to a local folder, setting your R working directory to that folder, and then running the following code in R:
library(GpGp); library(Matrix); library(RcppParallel)
library(parallel); library(sparseinv); library(fields)
for (nm in list.files('GPvecchia/R',pattern = "\\.[RrSsQq]$")) {
cat(nm,":"); source(file.path('GPvecchia/R',nm)); cat("\n")
}
Rcpp::sourceCpp('GPvecchia/src/U_NZentries.cpp')
Rcpp::sourceCpp('GPvecchia/src/MaxMin.cpp')
--->
This package can be installed directly from CRAN by running
install.packages("GPvecchia")
OMP library is required for the package to run fast. Mac OS users, where OMP has not been supported for a while could consult this page
Alternatively, the package can be installed using the github source. This can be done either by using devtools
devtools::install_github("katzfuss-group/GPvecchia")
or by downloading the repository and then building the package manually:
R CMD build GPvecchia
R CMD INSTALL GPvecchia_0.1.4.tar.gz
Note that Rtools is required for compiling C/C++ with OpenMP on Windows systems. When installing Rtools, the system PATH needs to be set so that the C++ compiler included in Rtools can be found by R. Once Rtools is installed, system('g++ -v')
can be used to check if the compiler is accessible from R.
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