Package: DPComb
Title: Discrete p-Value Combination Tests
Version: 1.0
Authors@R: c(
      person("Gonzalo", "Contador", email = "gonzalo.contador@usm.cl", role = c("aut", "cre")),
      person("Shuaichuan", "Feng", email = "sfeng2@wpi.edu", role = "aut"),
      person("Zheyang", "Wu", email = "zheyangwu@wpi.edu", role = "aut"))  
Depends: R (>= 3.5.0)
Description: 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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: MCMCpack
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-09-22 08:54:00 UTC; root
Author: Gonzalo Contador [aut, cre], Shuaichuan Feng [aut], Zheyang Wu
        [aut]
Maintainer: Gonzalo Contador <gonzalo.contador@usm.cl>
Repository: https://zwulab.r-universe.dev
Date/Publication: 2026-08-09 17:51:58 UTC
RemoteUrl: https://github.com/zwulab/dpcomb
RemoteRef: HEAD
RemoteSha: fb43a29645a0e6367f537edf899e81007feb04ce
Built: R 4.6.0; ; 2026-09-22 08:56:12 UTC; unix
