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accuracy: Tools for Accurate and Reliable Statistical Computing

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  • Altman, Micah
  • Gill, Jeff
  • McDonald, Michael P.

Abstract

Most empirical social scientists are surprised that low-level numerical issues in software can have deleterious effects on the estimation process. Statistical analyses that appear to be perfectly successful can be invalidated by concealed numerical problems. We have developed a set of tools, contained in accuracy, a package for R and S-PLUS, to diagnose problems stemming from numerical and measurement error and to improve the accuracy of inferences. The tools included in accuracy include a framework for gauging the computational stability of model results, tools for comparing model results, optimization diagnostics, and tools for collecting entropy for true random numbers generation.

Suggested Citation

  • Altman, Micah & Gill, Jeff & McDonald, Michael P., 2007. "accuracy: Tools for Accurate and Reliable Statistical Computing," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i01).
    • Handle: RePEc:jss:jstsof:v:021:i01
    DOI: http://hdl.handle.net/10.18637/jss.v021.i01
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    References listed on IDEAS

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    1. Marsaglia, George & Tsang, Wai Wan, 2002. "Some Difficult-to-pass Tests of Randomness," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i03).
    2. Altman, Micah & McDonald, Michael P., 2003. "Replication with Attention to Numerical Accuracy," Political Analysis, Cambridge University Press, vol. 11(3), pages 302-307, July.
    3. H. D. Vinod & B. D. McCullough, 1999. "The Numerical Reliability of Econometric Software," Journal of Economic Literature, American Economic Association, vol. 37(2), pages 633-665, June.
    4. Stromberg, Arnold, 2004. "Why Write Statistical Software? The Case of Robust Statistical Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 10(i05).
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    Cited by:

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