BMDU

Computes the Bayesian Dirichlet posterior for a multinomial vector
using unequal prior parameters. The prior is constructed by dividing
the categories into two groups, assigning random priors from different ranges
to simulate unequal information across categories.

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

BMDU function

<dl><dt>x</dt>
<dd>Integer vector of observed counts. Must be non-negative.</dd>
<dt>d</dt>
<dd>Integer scalar controlling how the categories are divided into
two groups for constructing unequal Dirichlet priors.</dd></dl>

Arguments

BMDU: Multinomial–Dirichlet Unequal Prior Bayesian Method — BMDU

<dl>

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


<dt>d</dt>
<dd>Integer scalar controlling how the categories are divided into
two groups for constructing unequal Dirichlet priors.</dd>

</dl>

BMDU: BMDU: Multinomial–Dirichlet Unequal Prior Bayesian Method

Description

Usage

Value

Arguments

Examples