Armadillo
Armadillo is a C++ linear algebra library aiming towards a good balance
between speed and ease of use. Integer, floating point and complex numbers
are supported, as well as a subset of trigonometric and statistics
functions. Various matrix decompositions are provided through optional
integration with LAPACK and ATLAS libraries.
A delayed evaluation approach is employed (during compile time) to combine
several operations into one and reduce (or eliminate) the need for
temporaries. This is accomplished through recursive templates and template
metaprogramming.
This library is useful if C++ has been decided as the language of choice
(due to speed and/or integration capabilities), rather than another language
like Matlab or Octave. It is distributed under a license that is useful in
both opensource and commercial contexts.
Armadillo is primarily developed by
Conrad Sanderson at
NICTA (Australia),
with contributions from around the world.
RcppArmadillo
RcppArmadillo
is an R package that facilitates using Armadillo classes
in R packages through Rcpp.
It achieves the integration by extending Rcpp’s
data interchange concepts to Armadillo classes.
Example
Here is a simple implementation of a fast linear regression (provided by
RcppArmadillo via the
fastLm() function):
Note however that you may not want to compute a linear regression fit this
way in order to protect from numerical inaccuracies on rankdeficient
problems. The help page for
fastLm()
provides an example.
Using RcppArmadillo in other packages
RcppArmadillo
is designed so that its classes can be used from other packages.
Using RcppArmadillo requires:

Using the header files provided by Rcpp and RcppArmadillo. This is
typically achieved by adding this line in the DESCRIPTION file of the
client package:LinkingTo : Rcpp, RcppArmadillo
and the following line in the package code:
#include <RcppArmadillo.h>

Linking against Rcpp dynamic or shared library and librairies needed
by Armadillo, which is achieved by adding this line in the src/Makevars
file of the client packagePKG_LIBS = $(shell $(R_HOME)/bin/Rscript e "Rcpp:::LdFlags()" ) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
and this line in the file src/Makevars.win:
PKG_LIBS = $(shell Rscript.exe e "Rcpp:::LdFlags()") $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
RcppArmadillo contains a function
RcppArmadillo.package.skeleton, modelled
after package.skeleton from the utils package in base R, that creates a
skeleton of a package using RcppArmadillo, including example code.
Quality Assurance
RcppArmadillo uses the RUnit package by Matthias Burger et al to provide
unit testing. RcppArmadillo currently has 19 unit tests (called from 8 unit
test functions).
Source code for unit test functions are stored in the unitTests directory
of the installed package and the results are collected in the
RcppArmadillounitTests vignette.
We run unit tests before sending the package to CRAN on as many systems as
possible, including Mac OSX (Snow Leopard), Debian, Ubuntu, Fedora 12
(64bit), Win 32 and Win64.
Unit tests can also be run from the installed package by executing
RcppArmadillo:::test()
where an output directory can be provided as an optional first argument.
Links
 Armadillo : http://arma.sourceforge.net/
 RcppArmadillo main page: http://dirk.eddelbuettel.com/code/rcpp.armadillo.html
 Rforge Rcpp project page: http://rforge.rproject.org/projects/rcpp/
 Dirk’s blog : http://dirk.eddelbuettel.com/blog/code/rcpp/
 Romain’s blog : http://romainfrancois.blog.free.fr/index.php?category/Rpackage/RcppArmadillo
Support
Questions about RcppArmadillo should be directed to the Rcppdevel mailing
list at
https://lists.rforge.rproject.org/cgibin/mailman/listinfo/rcppdevel
Questions about Armadillo itself should be directed to its forum
http://sourceforge.net/apps/phpbb/arma/
 Romain Francois, Montpellier, France Dirk Eddelbuettel, Chicago, IL, USA Doug Bates, Madison, WI, USA May 2010
Rbloggers.com offers daily email updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...