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The goal of cpp11eigen is to provide a novel approach to use the Eigen C++ library by using the header-only cpp11 R package and to simplify things for the end-user.
The idea is to pass matrices/vectors from R to C++, write pure C++/Eigen code for the computation, and then export the result back to R with the proper data structures.
This follows from the same goals as cpp11:
If this software is useful to you, please consider donating on Buy Me A Coffee. All donations
will be used to continue improving cpp11eigen
.
You can install the development version of cpp11eigen from GitHub:
::install_github("pachadotdev/cpp11eigen") remotes
I have provided a package template for RStudio that also works with VS Code.
The idea of this package is to be naive and simple (like me).
From RStudio/VSCode create a new project and run:
::pkg_template() cpp11eigen
Then follow the instructions from the README.
The vignettes contains detailed examples that I use to test
cpp11eigen
, these include Ordinary Least Squares.
Eigen supports OpenBLAS, Intel MKL, and the Accelerate framework (Mac). You can install OpenBLAS on Debian-based systems with:
sudo apt-get install libopenblas-dev
You can also use other commands for your specific operating system.
To verify that R is using OpenBLAS, you can run
sessionInfo()
after restarting R to check the BLAS/LAPACK
libraries in use:
: default
Matrix products: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 LAPACK
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.