{ "_id": "6a3571863efcd9bda43eb3d2", "Package": "DPComb", "Title": "Discrete p-Value Combination Tests", "Version": "1.0", "Authors@R": "c(\nperson(\"Gonzalo\", \"Contador\", email = \"gonzalo.contador@usm.cl\", role = c(\"aut\", \"cre\")),\nperson(\"Shuaichuan\", \"Feng\", email = \"sfeng2@wpi.edu\", role = \"aut\"),\nperson(\"Zheyang\", \"Wu\", email = \"zheyangwu@wpi.edu\", role = \"aut\"))", "Description": "Provides tools for performing p-value combination tests\nwith discrete input p-values. These tests combine significance\nevidence derived from independent discrete statistics to test a\nglobal null hypothesis, which is defined by the specified null\ndistribution(s) of these discrete statistics. The testing\nprocedure involves two main steps: (1) Wasserstein Adjustment:\nEach component of the combination statistic is replaced by an\nadjusted Z statistic. This adjustment, based on the minimum\nWasserstein distance, preserves the discrete nature of the\noriginal statistics while better aligning them with their\ncounterparts under continuity. (2) Calculation of the\nSignificance of the Combination Statistic: A continuous\ndistribution that optimally matches the discrete distribution\nof the combination statistic is obtained, and the testing\np-value for the global null hypothesis is computed. The first\nstep is analogous to Lancaster's approach but is generalized\nbased on Wasserstein optimization. The second step allows for\nasymptotic control of Type I error with higher statistical\npower. The package implements several p-value combination\nmethods, including Fisher’s, Pearson’s, George’s, Stouffer’s,\nand Edgington’s methods. The individual tests to be combined\ncan be right-sided, left-sided, or two-sided, and can be based\non binomial, Poisson, hypergeometric, noncentral\nhypergeometric, negative binomial, or geometric distributions,\nor a mixture of them. The underlying methodology and its\nfoundations are described in the following references:\nContador, Gonzalo and Wu, Zheyang (2025). A minimum Wasserstein\ndistance approach to Fisher's combination of independent,\ndiscrete p-values. Scandinavian Journal of Statistics, 52(3),\n1281-1300. Contador, Gonzalo and Wu,\nZheyang (2026). Optimal Adjustment and Combination of\nIndependent Discrete p-Values. Under revision at the Journal of\nComputational and Graphical Statistics.\n Lancaster, HO (1949). The\ncombination of probabilities arising from data in discrete\ndistributions. Biometrika, 36(3/4), 370-382.\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-19 16:40:14 UTC", "User": "root" }, "Author": "Gonzalo Contador [aut, cre], Shuaichuan Feng [aut], Zheyang Wu\n[aut]", "Maintainer": "Gonzalo Contador ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-19 12:10:17 UTC", "RemoteUrl": "https://github.com/cran/DPComb", "RemoteRef": "HEAD", "RemoteSha": "da489b53669c9913a9051cdeb1f9869b39c2395b", "MD5sum": "6dc6ac0904c672a8d7895903b7072122", "_user": "cran", "_type": "src", "_file": "DPComb_1.0.tar.gz", "_fileid": "4246c982b7066ad0c7c3b093d2ee3289e466d0d8fc388a7d7a3bd9821a837080", "_filesize": 262695, "_sha256": "4246c982b7066ad0c7c3b093d2ee3289e466d0d8fc388a7d7a3bd9821a837080", "_created": "2026-06-19T16:40:14.000Z", "_published": "2026-06-19T16:42:46.342Z", "_distro": "noble", "_jobs": 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