accuracy: Tools for Accurate and Reliable Statistical Computing

• Main Authors: Micah Altman, Jeff Gill, Michael P. McDonald
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2007-06-01
• Series: Journal of Statistical Software
• Subjects: sensitivity analysis; statistical computation; numerical accuracy; generalized inverse; generalized Cholesky; Starr test; global optimization
• Online Access: http://www.jstatsoft.org/v21/a01/paper

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.