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Multiple different\napproaches for integrating multiple environmental/genetic\nfactors, omics data, and/or phenotype data are implemented.\nThis includes functions for performing omics wide association\nstudies with one or more variables of interest as the exposure\nor outcome; a function for performing a meet in the middle\nanalysis for linking exposures, omics, and outcomes (as\ndescribed by Chadeau-Hyam et al., (2010)\n); and a function for\nperforming a mixtures analysis across all omics features using\nquantile-based g-Computation (as described by Keil et al.,\n(2019) ).", "License": "GPL (>= 3)", "Config/testthat/edition": "3", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "cmake make libicu-dev", "Repository": "https://goodrich-lab.r-universe.dev", "Date/Publication": "2025-02-14 04:44:14 UTC", "RemoteUrl": "https://github.com/goodrich-lab/epiomics", "RemoteRef": "HEAD", "RemoteSha": 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