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:

1. interfacing R with C++ manually via .Call();

2. using the Rcpp package for easier/safer integration between R and C++;

3. using Rcpp sugar for performing standard statistical operations in C++;

4. performing numerical linear algebra computation via the RcppArmadillo package;

5. including C++ code in an R package via Rcpp;

6. parallelizing your C++ code via RcppParallel.

Some of the chapters contain programming exercises, focussed on exploiting the Rcpp family of packages to speed-up statistical computations.

References:

• Allaire, J.J., Eddelbuettel, D. and François, R., 2018. Rcpp Attributes. Vignette included in R package Rcpp, URL http://CRAN.R-Project.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.28-36.
• Eddelbuettel, D. and François, R., 2010. Rcpp syntactic sugar.
• Eddelbuettel, D. and Sanderson, C., 2014. RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics & Data Analysis, 71, pp.1054-1063.
• 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.