Ross Ihaka

R: A Language for Data Analysis and Graphics

Abstract In this article we discuss our experience designing and implementing a statistical computing language. In developing this new language, we sought to combine what we felt were useful features from two existing computer languages. We feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scoping.

Lexical Scope and Statistical Computing

Abstract Programming environments such as S and Lisp-Stat have languages for performing computations, data storage mechanisms, and a graphical interface. These languages provide an interactive interface to data analysis that is invaluable. To take full advantage of these programming environments, statisticians must understand the differences between them. Ihaka and Gentleman introduced R, a version of S which uses a different scoping regimen. In some ways this makes R behave more like Lisp-Stat. This article discusses the concept of scoping rules and shows how lexical scope can enhance the functionality of a language.

Ink features for diagram recognition

The ability to automatically recognize a sketch accurately is important to computer-based diagramming. Many recognition techniques have been proposed but few researchers have reported the use of formal methods to select the most appropriate ink features for recognition algorithms. We have used a statistical approach to identify the most important distinguishing features of ink for dividing text and shapes. We implemented these into an existing recognition engine and conducted a comparative evaluation. Our feature set more successfully classified a range of common diagram elements than two existing dividers.

An Approach to Providing Mathematical Annotation in Plots

Abstract A simple method for providing mathematical annotation of plots produced with the R environment is described. Although the implementation is specific to R, a similar method could be used in any environment which uses an expression-based command interface and provides a basic quoting mechanism.

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

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.

Statistical aspects of earthquake source parameter estimation in the presence of signal generated noise

Seismologists have developed models which relate observed ground motion to the physical parameters of the earthquake which caused it These models are based on an idealized model which approximates the behavior of the real earth. In this paper we will examine some of the uncertainties which cause observations to deviate from such models. These uncertainties include presence of contaminating noise and the wave scattering caused by the inhomogeneous nature of the earth. By modeling such uncertainties statistically, we are able to formulate a maximum -likelihood type procedure for model fitting and address the such issues as of the computation of standard errors and graphical diagnostics for goodness-of-fit. The paper illustrates the procedure on observations from a small Califomian earthquake.

Java and Dylan

Abstract This article compares two new computer languages, Dylan and Java. The comparison is based on their relative merits in terms of statistical computing.

Developing Statistical Software in Fortran 95

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