To use graphics efficiently in an automatic report generation system, one has to model messages and how they pass from the writer (intention) to the reader (interpretation). This paper describes PostGraphe a system which generates a report integrating graphics and text from a set of writer's intentions. The system is given the data in tabular form as might be found in a spreadsheet; also input is a declaration of the types of values in the columns of the table. The user then indicates the intentions to be conveyed in the graphics (e.g. compare two variables or show the evolution of a set of variables) and the system generates a report in L A T E X with the appropriate PostScript graphic files. PostGraphe uses the same information to generate the accompanying text that helps the reader to focus on the important points of the graphics. We also describe how these ideas have been embedded to create a new Chart Wizard for Microsoft Excel. 1 Introduction: important factors in the generati...

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The paper describes two separate but synergistic tools for running experiments on large Lisp programs. The first tool,

Dimensionality is an issue that can arise in every scientific field. Generally speaking, the difficulty lies on how to visualize a high dimensional function or data set. This is an area which has become increasingly more important due to the advent of computer and graphics technology. People often ask: “How do they look?”, “What structures are there?”, “What model should be used? ” Aside from the differences that underly the various scientific contexts, such kind of questions do have a common root in Statistics. This should be the driving force for the study of high dimensional data analysis. Sliced inverse regression(SIR) and principal Hessian direction(PHD) are two basic dimension reduction methods. They are useful for the extraction of geometric information underlying noisy data of several dimensions- a crucial step in empirical model building which has been overlooked in the literature. In this Lecture Notes, I will review the theory of SIR/PHD and describe some ongoing research in various application areas. There are two parts. The first part is based on materials that have already appeared in the literature. The second part is just a collection of some manuscripts which are not yet published. They are included here for completeness.

this paper we discuss visual statistical analysis using ViSta. ViSta is designed for an audience of users having a very wide range of data analysis sophistication, ranging from novice to expert. ViSta provides seamlessly integrated data analysis environments specifically tailored to the user's level of expertise. Visual guidance is available for novices (such as students), and visual authoring tools are available for experts (such as teachers) to create guidance for these novices. A structured graphical user interface is available for competent users, and a command line interface is available for sophisticated users. The complete Lisp-Stat (Tierney, 1990) programming environment is available to researchers and graduate students who wish to extend ViSta's capabilities.

This paper examines work in “computing with data”—in computing support for scientific and other activities to which statisticians can contribute. Relevant computing techniques, besides traditional statistical computing, include data management, visualization, interactive languages and user-interface design. The paper emphasizes the concepts underlying computing with data, with emphasis on how those concepts can help in practical work. We look at past, present, and future: some concepts as they arose in the past and as they have proved valuable in current software; applications in the present, with one example in particular, to illustrate the challenges these present; and new directions for future research, including one exciting joint project.

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The difference between displaying networks with 100–1,000 nodes and displaying ones with 10,000–100,000 nodes is not merely quantitative, it is qualitative. Layout algorithms suitable for the former are too slow for the latter, requiring new algorithms or modified (often relaxed) versions of existing algorithms to be invented. The density of nodes and edges displayed per inch of screen real estate requires special visual techniques to filter the graphs and focus attention. Compounding the problem is that large real-life networks are often weighted graphs and usually have additional data associated with the nodes and edges. A system for investigating and exploring such large, complex datasets needs to be able to display both graph structure and node and edge attributes so that patterns and information hidden in the data can be seen. In this article we describe a tool that addresses these needs, the NicheWorks tool. We describe and comment on the available layout algorithms and the linked views interaction system, and detail two examples of the use of NicheWorks for analyzing Web sites and detecting international telephone fraud.

is a language similar to for statistical data analysis, based on modern programming concepts and released under the GNU General Public License. It permits the integration of program scripts with compiled dynamically loaded libraries of functions when computing speed is important. Following a broad outline of existing collections of functions for spatial statistics written for , we show how they may be ported to , and compare their characteristics. We further demonstrate how existing work may be extended to topics not yet covered, and how libraries of functions may be constructed.

Emacs Speaks Statistics (ESS) is a user interface for developing statistical applications and performing data analysis using any of several common statistical programming languages. ESS falls in the programming tools category of Integrated Development Environments (IDEs), which are approaches for developing and visualizing computer programs. We discuss how it works, the advantages of using it, and extensions for increasing statistical programming efficiency. Keywords: Data Analysis, Programming Tools, User Interfaces, SAS, S-PLUS, R, XLISPSTAT, STATA 1