cpp11eigen

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:

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0