Reliability analysts are typically forced to choose between using an "algorithmic programming language" or a "reliability package" for analyzing their models and lifetime data. This paper shows that computational languages can be used to bridge the gap to combine the flexibility of a programming language with the ease of use of a package. Computational languages facilitate the development of new statistical techniques and are excellent teaching tools. This paper considers three diverse reliability problems that are handled easily with a computational algebra language: system reliability bounds, lifetime data analysis, and model selection.
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