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:
- Enforcing copy-on-write semantics.
- Improving the safety of using the R API from C++ code.
- Using UTF-8 strings everywhere.
- Applying newer C++11 features.
- Having a more straightforward, simpler implementation.
- Faster compilation time with lower memory requirements.
- Growing vectors more efficiently.
If this software is useful to you, please consider donating on Buy Me A Coffee. All donations will be used to continue improving cpp11eigen
.
Installation
You can install the development version of cpp11eigen from GitHub:
remotes::install_github("pachadotdev/cpp11eigen")
Examples
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:
cpp11eigen::pkg_template()
Then follow the instructions from the README.
The vignettes contains detailed examples that I use to test cpp11eigen
, these include Ordinary Least Squares.
Linear Algebra Libraries
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