Back to the Future: Lisp as a Base for a Statistical Computing System

Ross Ihaka and Duncan Temple Lang
Additional contact information
Ross Ihaka: University of Auckland
Duncan Temple Lang: University of California

A chapter in COMPSTAT 2008, 2008, pp 21-33 from Springer

Abstract: Abstract The application of cutting-edge statistical methodology is limited by the capabilities of the systems in which it is implemented. In particular, the limitations of R mean that applications developed there do not scale to the larger problems of interest in practice. We identify some of the limitations of the computational model of the R language that reduces its effectiveness for dealing with large data efficiently in the modern era. We propose developing an R-like language on top of a Lisp-based engine for statistical computing that provides a paradigm for modern challenges and which leverages the work of a wider community. At its simplest, this provides a convenient, high-level language with support for compiling code to machine instructions for very significant improvements in computational performance. But we also propose to provide a framework which supports more computationally intensive approaches for dealing with large datasets and position ourselves for dealing with future directions in high-performance computing. We discuss some of the trade-offs and describe our efforts to realizing this approach. More abstractly, we feel that it is important that our community explore more ambitious, experimental and risky research to explore computational innovation for modern data analyses.

Keywords: Lisp; optional typing; performance (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_2

Ordering information: This item can be ordered from
http://www.springer.com/9783790820843

DOI: 10.1007/978-3-7908-2084-3_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().