Statistical Computing 2
This website contains teaching materials for part of the second computing unit of the taught component of the Computational Statistics and Data Science (COMPASS) PhD programme. The source for this website is available here. The website for the first computing unit can be found here.
The material provided here focusses on how R
can be interfaced with C++
via the Rcpp
package. More specifically, we guide the students through the following steps:

interfacing
R
withC++
manually via.Call()
; 
using the
Rcpp
package for easier/safer integration betweenR
andC++
; 
using
Rcpp sugar
for performing standard statistical operations inC++
; 
performing numerical linear algebra computation via the
RcppArmadillo
package; 
including
C++
code in anR
package viaRcpp
; 
parallelizing your
C++
code viaRcppParallel
.
Some of the chapters contain programming exercises, focussed on exploiting the Rcpp
family of packages to speedup statistical computations.
References:
 Allaire, J.J., Eddelbuettel, D. and François, R., 2018. Rcpp Attributes. Vignette included in R package Rcpp, URL http://CRAN.RProject.org/package=Rcpp.
 Chambers, J.M., 2017. Extending R. Chapman and Hall/CRC.
 Eddelbuettel, D., 2013. Seamless R and C++ integration with Rcpp. New York: Springer.
 Eddelbuettel, D. and Balamuta, J.J., 2018. Extending R with C++: A Brief Introduction to Rcpp. The American Statistician, 72(1), pp.2836.
 Eddelbuettel, D. and François, R., 2010. Rcpp syntactic sugar.
 Eddelbuettel, D. and Sanderson, C., 2014. RcppArmadillo: Accelerating R with highperformance C++ linear algebra. Computational Statistics & Data Analysis, 71, pp.10541063.
 Matloff, N., 2015. Parallel computing for data science: with examples in R, C++ and CUDA. Chapman and Hall/CRC.
 Wickham, H., 2014. Advanced r. Chapman and Hall/CRC.