mixture_example

Simulated dataset for testing the <code>est_mixture()</code> function.
Each row corresponds to one study providing summary-level data
from a two-component normal mixture.

datasets

Provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies.
The package supports three modules: (1) bivariate correlation estimation for binary outcomes, (2) bivariate correlation estimation for continuous outcomes, and
(3) estimation of component-wise means and variances under a conditional two-component Gaussian mixture model for a continuous variable stratified by a binary class label.
These methods enable privacy-preserving joint estimation when individual-level data are unavailable.
The approaches are detailed in Shang, Tsao, and Zhang (2025a) <doi:10.48550/arXiv.2505.03995> and Shang, Tsao, and Zhang (2025b) <doi:10.48550/arXiv.2508.02057>.

Longwen Shang

ebdm

Estimating Bivariate Dependency from Marginal Data

Min Tsao

Xuekui Zhang

mixture_example function

A data frame with 4 columns:<dl>
 <dt>ni</dt>
<dd>Sample size for each study.</dd> <dt>mi</dt>
<dd>Count of group 1 individuals in each study.</dd> <dt>xbar</dt>
<dd>Sample mean of the outcome variable.</dd> <dt>s2</dt>
<dd>Sample variance of the outcome variable.</dd>
</dl>

Format

Example Data: Mixture Model Summaries — mixture_example

A data frame with 4 columns:<dl>
 <dt>ni</dt>
<dd>Sample size for each study.</dd>

 <dt>mi</dt>
<dd>Count of group 1 individuals in each study.</dd>

 <dt>xbar</dt>
<dd>Sample mean of the outcome variable.</dd>

 <dt>s2</dt>
<dd>Sample variance of the outcome variable.</dd>


</dl>

mixture_example: Example Data: Mixture Model Summaries

Description

Usage

Arguments

Format