{ "_id": "6a10427facfb0bcc41c9dbb5", "Package": "SetTest", "Type": "Package", "Title": "Group Testing Procedures for Signal Detection and\nGoodness-of-Fit", "Version": "0.3.0", "Author": "Hong Zhang and Zheyang Wu", "Maintainer": "Hong Zhang ", "Description": "It provides cumulative distribution function (CDF),\nquantile, p-value, statistical power calculator and random\nnumber generator for a collection of group-testing procedures,\nincluding the Higher Criticism tests, the one-sided\nKolmogorov-Smirnov tests, the one-sided Berk-Jones tests, the\none-sided phi-divergence tests, etc. The input are a group of\np-values. The null hypothesis is that they are i.i.d.\nUniform(0,1). In the context of signal detection, the null\nhypothesis means no signals. In the context of the\ngoodness-of-fit testing, which contrasts a group of i.i.d.\nrandom variables to a given continuous distribution, the input\np-values can be obtained by the CDF transformation. The null\nhypothesis means that these random variables follow the given\ndistribution. For reference, see [1]Hong Zhang, Jiashun Jin and\nZheyang Wu. \"Distributions and power of optimal\nsignal-detection statistics in finite case\", IEEE Transactions\non Signal Processing (2020) 68, 1021-1033; [2] Hong Zhang and\nZheyang Wu. \"The general goodness-of-fit tests for correlated\ndata\", Computational Statistics & Data Analysis (2022) 167,\n107379.", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.2.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 07:20:03 UTC", "User": "root" }, "Repository": "https://wpihongzhang.r-universe.dev", "Date/Publication": "2024-07-16 02:45:47 UTC", "RemoteUrl": "https://github.com/cran/SetTest", "RemoteRef": "HEAD", "RemoteSha": "191c7818bbf3d729ada49829027720c9f9f71079", "MD5sum": "7e5b8487218dee32295b860c69ebf8da", "_user": "wpihongzhang", "_type": "src", "_file": "SetTest_0.3.0.tar.gz", "_fileid": "791ec002b08f018ed3bf38cd77397770496b14760612c5fff60af1ca99f8798b", "_filesize": 219572, "_sha256": 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