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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. Altman, Micah & McDonald, Michael P., 2003. "Replication with Attention to Numerical Accuracy," Political Analysis, Cambridge University Press, vol. 11(3), pages 302-307, July.
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    Cited by:

    1. Santosh Bangalore, Sai & Wang, Jelai & Allison, David B., 2009. "How accurate are the extremely small P-values used in genomic research: An evaluation of numerical libraries," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2446-2452, May.

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