The Appell function of the first kind, which is a two variable extension of the hypergeometric distribution.

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Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.

Derek S. Young

tolerance

Statistical Tolerance Intervals and Regions

Kedai Cheng

F1 function

<dl> <dt>a, b, b.prime, c</dt>
<dd>Appropriate parameters for this function.</dd> <dt>x, y</dt>
<dd>The inputted values to evaluate this function such that each is less than 1 in absolute value.</dd> <dt>...</dt>
<dd>Additional arguments passed to the <code>integrate</code> function.</dd></dl>

Arguments

Appell's F1 Hypergeometric Function — F1

<dl>

 <dt>a, b, b.prime, c</dt>
<dd>Appropriate parameters for this function.</dd>

 <dt>x, y</dt>
<dd>The inputted values to evaluate this function such that each is less than 1 in absolute value.</dd>

 <dt>...</dt>
<dd>Additional arguments passed to the <code>integrate</code> function.</dd>

</dl>

F1: Appell's F1 Hypergeometric Function

Description

Usage

Value

Arguments

References

See Also

Examples