ebdm: Estimating Bivariate Dependency from Marginal Data
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>.
| Version: |
3.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats |
| Published: |
2025-10-16 |
| DOI: |
10.32614/CRAN.package.ebdm |
| Author: |
Longwen Shang [aut, cre],
Min Tsao [aut],
Xuekui Zhang [aut] |
| Maintainer: |
Longwen Shang <shanglongwen0918 at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
ebdm results |
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