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Abstract Since its introduction, APL has frequently been touted as an ideal programming language for statistical applications. Among the attractive features of APL for statistics are its extensibility, the presence of primitives for operations such as sorting, matrix inversion, and arranging data, and powerful facilities for handling matrices and other arrays. Newer programming languages incorporating some of the features of APL—notably New S and XLisp-Stat—have been designed specifically for statistical applications. Is there still a niche for APL? We believe that APL2 continues to offer some significant benefits for statistical computation, including user-defined operators, nested arrays, and convenient implementation of arrays of any dimension. We use these characteristics of the language in the course of designing an extensible computing environment for data analysis and programming based on APL2 that incorporates some of the features of modern statistical programming languages, such as data objects, symbolic model specification, missing-data handling, and automatic search of a path of files. The system, which includes interactive statistical graphics, general linear models, and robust estimation methods, has been implemented using IBM's APL2 for PC-compatibles—both the standard version of this system and the freeware TryAPL2. The latter provides students with a free environment for modern data analysis and one in which they can explore the design of statistical software.
Friendly et al. (Thu,) studied this question.