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cpp11armadillo

R-CMD-check codecov BuyMeACoffee CRAN status

The goal of cpp11armadillo is to provide a novel approach to use the Armadillo 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++/Armadillo 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 cpp11armadillo.

Installation

You can install the released version of cpp11armadillo from CRAN:

install.packages("cpp11armadillo")

You can install the development version of cpp11armadillo from GitHub:

remotes::install_github("pachadotdev/cpp11armadillo")

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:

cpp11armadillo::pkg_template()

Then follow the instructions from the README.

The vignettes contains detailed examples that I use to test cpp11armadillo, these include Ordinary Least Squares, eigenvalues, and the Capital Asset Pricing Model (CAPM).

Linear Algebra Libraries

Armadillo 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

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.