Computes Wilson score-type simultaneous confidence intervals for multinomial
proportions. The Wilson method improves upon the Wald interval by ensuring
better coverage probabilities, especially for small samples or proportions
near 0 or 1.

Several authors have proposed methods for constructing simultaneous confidence intervals for multinomial proportions. The package implements seven classical approaches—Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glaz—along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation.

originally by Subbiah M  packaged by Sumathi R

CoinMinD

Simultaneous Confidence Intervals for Multinomial Proportions

Subbiah M

WS function

<dl><dt>inpmat</dt>
<dd>Integer vector of observed cell counts corresponding to a
categorical dataset. All values must be non-negative.</dd>
<dt>alpha</dt>
<dd>Desired statistical significance level</dd></dl>

Arguments

Author

WS: Wilson Score Method for Simultaneous Confidence Intervals — WS

<dl>

<dt>inpmat</dt>
<dd>Integer vector of observed cell counts corresponding to a
categorical dataset. All values must be non-negative.</dd>


<dt>alpha</dt>
<dd>Desired statistical significance level</dd>

</dl>

WS: WS: Wilson Score Method for Simultaneous Confidence Intervals

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples