Lost Opportunities: Why We Need a Variety of Statistical Languages
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Abstract
To the worker who only has a hammer, we are told, everything looks like a nail. Solutions to applied statistical problems are framed by the limitations imposed by statistical computing packages and languages. For better or worse, we can do what the packages do; we cannot do what the packages won't do. Statistical languages like R have basic tools that allow the analyst to design new hammers, but even in R we cannot build an arbitrary hammer, only ones within the limits imposed by the R language. XLISP-STAT imposes different limitations, so we can produce different hammers.
In this article, I look at some of the tools in XLISP-STAT that allow the user to think about graphics in ways that cannot be easily replicated in other statistical languages. The interactive graphical methods available in XLISP-STAT lead to very different methodology than would be developed without the tools that XLISP-STAT provides. The general approach to graphics and indeed to data analysis in general is quite different in a package like Arc that is built on top of XLISP-STAT, than it is in other statistical packages. We discuss why that might be true, and why this depends on design options created by XLISP-STAT.
In this article, I look at some of the tools in XLISP-STAT that allow the user to think about graphics in ways that cannot be easily replicated in other statistical languages. The interactive graphical methods available in XLISP-STAT lead to very different methodology than would be developed without the tools that XLISP-STAT provides. The general approach to graphics and indeed to data analysis in general is quite different in a package like Arc that is built on top of XLISP-STAT, than it is in other statistical packages. We discuss why that might be true, and why this depends on design options created by XLISP-STAT.