Discrete p-Value Combination Tests
Overview
Provides tools for performing p-value combination tests with discrete input p-values. These tests combine significance evidence derived from independent discrete statistics to test a global null hypothesis, which is defined by the specified null distribution(s) of these discrete statistics. The testing procedure involves two main steps: (1) Wasserstein Adjustment: Each component of the combination statistic is replaced by an adjusted Z statistic. This adjustment, based on the minimum Wasserstein distance, preserves the discrete nature of the original statistics while better aligning them with their counterparts under continuity. (2) Calculation of the Significance of the Combination Statistic: A continuous distribution that optimally matches the discrete distribution of the combination statistic is obtained, and the testing p-value for the global null hypothesis is computed. The first step is analogous to Lancaster's approach but is generalized based on Wasserstein optimization. The second step allows for asymptotic control of Type I error with higher statistical power. The package implements several p-value combination methods, including Fisher’s, Pearson’s, George’s, Stouffer’s, and Edgington’s methods. The individual tests to be combined can be right-sided, left-sided, or two-sided, and can be based on binomial, Poisson, hypergeometric, noncentral hypergeometric, negative binomial, or geometric distributions, or a mixture of them. The underlying methodology and its foundations are described in the following references: Contador, Gonzalo and Wu, Zheyang (2025). A minimum Wasserstein distance approach to Fisher's combination of independent, discrete p-values. Scandinavian Journal of Statistics, 52(3), 1281-1300. doi:10.1111/sjos.12787 Contador, Gonzalo and Wu, Zheyang (2026). Optimal Adjustment and Combination of Independent Discrete p-Values. Under revision at the Journal of Computational and Graphical Statistics. doi:10.48550/arXiv.2508.02647 Lancaster, HO (1949). The combination of probabilities arising from data in discrete distributions. Biometrika, 36(3/4), 370-382. doi:10.1093/biomet/36.3-4.370.
- New maintainer: Zheyang Wu (<zheyangwu@wpi.edu>).
- The package now has a public repository and issue tracker: <https://github.com/ZWuLab/DPComb>. Added
URLandBugReportsfields toDESCRIPTION. - No changes to functions or computations.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK10 OK2026-06-22
- ERROR5 OK, 1 ERROR2026-06-20
Documentation
- Help pages
- 9
- Examples that run
- 100%9 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 22%
Downloads
Repository
Checks run against github.com/zwulab/dpcomb on 2026-10-18.
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Dependencies
Code & Tests
Datasets
People & History
Previously maintained by Gonzalo Contador (1.0).
Pick two releases to compare their code metrics.
- 1.0.1Latest
- 1.02026-06-19
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-19
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 1.1 KB / 1 file
- Download size
- 240 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("DPComb")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1111/sjos.12787, 10.1093/biomet/36.3-4.370.
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