tolerance: Statistical Tolerance Intervals and Regions

Synopsis

The tolerance package provides functions for estimating tolerance limits (intervals) for various univariate distributions, Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings, and analysis of variance tolerance intervals. Visualizations in the form of histograms, scatterplots, and control charts are also available for many of these settings. More details about the package are included in both the original JSS article as well as a subsequent Handbook of Statistics book chapter.

Other highlights:

• Includes calculations for tolerance limits (intervals) for numerous continuous and discrete distributions.

• Pointwise tolerance interval calculations for linear, nonlinear, nonparametric, and multivariate regression settings are available.

• Functions for sample size determination in normal and nonparametric settings are available.

• Includes a novel operating characteristic curve function regarding k-factors for tolerance intervals based on normality.

• Novel nonparametric methods are included, such as the ability to construct multivariate hyperrectangular tolerance regions for setting reference regions.

Documentation

The JSS article and the Handbook of Statistics book chapter both provide documentation about the tolerance package. The RJ article provides an extensive overview of most of the normal-based procedures available within the tolerance package. Moreover, the help file also documents the references used for each function.

Examples

Additional examples for the tolerance package are currently being developed for a Shiny app.

Installation

Released and tested versions of tolerance are available via the CRAN network, and can be installed from within R via

install.packages("tolerance")

Support

The issue tickets at the GitHub repo are the primary bug reporting interface. As with the other web resources, previous issues can be searched as well.

Authors

Derek S. Young

License

GPL (>= 2)

Funding Acknowledgment

This package is based upon work supported by the Chan Zuckerberg Initiative, Grant Number 2020-225193.

Code of Conduct

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