Why Write Statistical Software? The Case of Robust Statistical Methods
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Abstract
Robust statistical methods are designed to work well when classical assumptions, typically normality and/or the lack of outliers, are violated. Almost everyone agrees on the value of robust statistical procedures. Nonetheless, after more than 40 years and thousands of papers, few robust methods were available in standard statistical software packages until very recently.
This paper argues that one of the primary reasons for the lack of robust statistical methods in standard statistical software packages is the fact that few developers of statistical methods are willing to write user-friendly and readable software for the methods they develop, regardless of the usefulness of the method. Recent changes in academic statistics make it highly desirable for all developers of statistical methods to provide usable code for their statistical methods.
This paper argues that one of the primary reasons for the lack of robust statistical methods in standard statistical software packages is the fact that few developers of statistical methods are willing to write user-friendly and readable software for the methods they develop, regardless of the usefulness of the method. Recent changes in academic statistics make it highly desirable for all developers of statistical methods to provide usable code for their statistical methods.