Computational Statistics

, Volume 24, Issue 2, pp 217–223 | Cite as

Code analysis and parallelizing vector operations in R

  • Luke TierneyEmail author
Original Paper


This paper presents some current work and preliminary thoughts on two seemingly unrelated areas. The first is the development of code analysis tools to help identify possible errors in R code. Current versions of these tools have been useful in finding bugs in R’s code as well as code in packages submitted to CRAN. The second area, where work is just beginning, is the development of mechanisms to allow R’s internal vectorized operations, as well as vectorized operations defined in packages, to take advantage of multiple processors. These two areas are related through their connections to ongoing efforts to develop a byte code compiler for R.


Static analysis Compilation Software testing Parallel computation 


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  1. Becker RA, Chambers JM, Wilks AR (1988) The new S language: a programming environment for data analysis and graphics. Wadsworth, BelmontGoogle Scholar
  2. Burger M, Juenemann K, Koenig T (2006) RUnit: R Unit test framework. R package version 0.4.14Google Scholar
  3. Butenhof DR (1997) Programming with POSIX threads. Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
  4. Chambers JM (1998) Programming with data: a guide to the S language. Springer, HeidelbergzbMATHGoogle Scholar
  5. Falcon S, Gentleman R (2007) The weaver package: tools and extensions for processing Sweave documents. In: Proceedings of DSC 2007Google Scholar
  6. Kremenek T, Engler DR (2003) Z-ranking: using statistical analysis to counter the impact of static analysis approximations. In: Cousot R (eds) SAS. Lecture Notes in Computer Science, vol 2694. Springer, Heidelberg, pp 295–315Google Scholar
  7. Kremenek T, Ashcraft K, Yang J, Engler D (2004) Correlation exploitation in error ranking. SIGSOFT Softw Eng Notes 29(6): 83–93CrossRefGoogle Scholar
  8. Nielson F, Nialson HR, Hankin C (1998) Principles of program analysis. Springer, HeidelbergGoogle Scholar
  9. R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0Google Scholar
  10. Tierney L (2001) A preliminary report. In: Proceedings of the 2nd international workshop on distributed statistical computingGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Statistics and Actuarial ScienceUniversity of IowaIowa CityUSA

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