{ "_id": "6acf4cc8d9de2f3f33fca8a2", "Package": "csmGmm", "Type": "Package", "Title": "Conditionally Symmetric Multidimensional Gaussian Mixture Model", "Version": "0.5.0", "Authors@R": "c(\nperson(\"Ryan\", \"Sun\", email = \"ryansun.work@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Emily\", \"Kim\", role=\"aut\"))", "Description": "Implements the conditionally symmetric multidimensional\nGaussian mixture model (csmGmm) for large-scale testing of\ncomposite null hypotheses in genetic association applications\nsuch as mediation analysis, pleiotropy analysis, and\nreplication analysis. In such analyses, we typically have J\nsets of K test statistics where K is a small number (e.g. 2 or\n3) and J is large (e.g. 1 million). For each one of the J sets,\nwe want to know if we can reject all K individual nulls. Please\nsee the vignette for a quickstart guide. The paper describing\nthese methods is \"Testing a Large Number of Composite Null\nHypotheses Using Conditionally Symmetric Multidimensional\nGaussian Mixtures in Genome-Wide Studies\" by Sun R, McCaw Z, &\nLin X (Journal of the American Statistical Association 2025,\n).", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-14 08:41:05 UTC", "User": "root" }, "Author": "Ryan Sun [aut, cre], Emily Kim [aut]", "Maintainer": "Ryan Sun ", "Config/pak/sysreqs": "libssl-dev", "Repository": "https://ryansunwork.r-universe.dev", "Date/Publication": "2026-06-16 17:26:37 UTC", "RemoteUrl": "https://github.com/cran/csmGmm", "RemoteRef": "HEAD", "RemoteSha": "fd3fd9167e83020f905639cca271f2bdd75095a1", "_user": "ryansunwork", "_type": "src", "_file": "csmGmm_0.5.0.tar.gz", "_fileid": "https://r2.ropensci.org/03dbdbccf42e70a7303733f8efd132e0456e0f003ef52ebfe6f7a1dd79c3d89f", "_filesize": 163583, 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