{ "_id": "6a1d6b2a1d7bb097a0a4f567", "Type": "Package", "Package": "causens", "Title": "Perform Causal Sensitivity Analyses Using Various Statistical\nMethods", "Version": "0.0.3", "Authors@R": "c(person(\"Larry\", \"Dong\", NULL, \"larry.dong@mail.utoronto.ca\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-7775-7798\")),\nperson(\"Yushu\", \"Zou\", NULL, \"yushu.zou@mail.utoronto.ca\", role = c(\"aut\"), comment = c(ORCID = \"0009-0004-1133-4724\")),\nperson(\"Kuan\", \"Liu\", NULL, \"kuan.liu@utoronto.ca\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-5017-1276\")))", "Description": "While data from randomized experiments remain the gold\nstandard for causal inference, estimation of causal estimands\nfrom observational data is possible through various confounding\nadjustment methods. However, the challenge of unmeasured\nconfounding remains a concern in causal inference, where\nfailure to account for unmeasured confounders can lead to\nbiased estimates of causal estimands. Sensitivity analysis\nwithin the framework of causal inference can help adjust for\npossible unmeasured confounding. In `causens`, three main\nmethods are implemented: adjustment via sensitivity functions\n(Brumback, Hernán, Haneuse, and Robins (2004)\n and Li, Shen, Wu, and Li (2011)\n), Bayesian parametric modelling and\nMonte Carlo approaches (McCandless, Lawrence C and Gustafson,\nPaul (2017) ).", "License": "MIT + file LICENSE", "URL": "https://kuan-liu-lab.github.io/causens/,\nhttps://github.com/Kuan-Liu-Lab/causens", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "BugReports": "https://github.com/Kuan-Liu-Lab/causens/issues", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "Repository": "https://kuan-liu-lab.r-universe.dev", "Date/Publication": "2025-06-03 21:54:12 UTC", "RemoteUrl": "https://github.com/kuan-liu-lab/causens", "RemoteRef": "HEAD", "RemoteSha": "a5c37d7fc5d8ac1e5dc8be9c14a41f49a55ae5f2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 11:16:47 UTC", "User": "root" }, "Author": "Larry Dong [aut, cre] (ORCID: ),\nYushu Zou [aut] (ORCID: ),\nKuan Liu [aut] 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