BMDE

Computes the Bayesian Dirichlet posterior for a multinomial vector
with equal prior parameters and returns the posterior mean, 95
and the volume of those intervals.

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

BMDE function

<dl><dt>x</dt>
<dd>Integer vector of observed counts. Must be non-negative.</dd>
<dt>p</dt>
<dd>Numeric scalar or vector specifying Dirichlet prior parameters. Must be non-negative.</dd></dl>

Arguments

BMDE: Multinomial–Dirichlet Equal Prior Bayesian Method — BMDE

<dl>

<dt>x</dt>
<dd>Integer vector of observed counts. Must be non-negative.</dd>


<dt>p</dt>
<dd>Numeric scalar or vector specifying Dirichlet prior parameters. Must be non-negative.</dd>

</dl>

BMDE: BMDE: Multinomial–Dirichlet Equal Prior Bayesian Method

Description

Usage

Value

Arguments

Examples