{ "_id": "6ac75fd5de46216d5edcf0f7", "Package": "SDModels", "Title": "Spectrally Deconfounded Models", "Version": "2.0.2", "Authors@R": "c(\nperson(\"Markus\", \"Ulmer\", email = \"markus.ulmer@stat.math.ethz.ch\",\nrole = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0001-7783-8475\")),\nperson(\"Cyrill\", \"Scheidegger\", role = \"aut\", comment = c(ORCID = \"0009-0005-2851-1384\")))", "Description": "Screen for and analyze non-linear sparse direct effects in\nthe presence of unobserved confounding using the spectral\ndeconfounding techniques (Ćevid, Bühlmann, and Meinshausen\n(2020), Guo, Ćevid, and\nBühlmann (2022) ). These methods have\nbeen shown to be a good estimate for the true direct effect if\nwe observe many covariates, e.g., high-dimensional settings,\nand we have fairly dense confounding. Even if the assumptions\nare violated, it seems like there is not much to lose, and the\ndeconfounded models will, in general, estimate a function\ncloser to the true one than classical least squares\noptimization. 'SDModels' provides functions SDAM() for\nSpectrally Deconfounded Additive Models (Scheidegger, Guo, and\nBühlmann (2025) ) and SDForest() for\nSpectrally Deconfounded Random Forests (Ulmer, Scheidegger, and\nBühlmann (2025) ).", "License": "GPL-3", "RdMacros": "Rdpack", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "URL": "https://www.markus-ulmer.ch/SDModels/", "BugReports": "https://github.com/markusul/SDModels/issues", "Config/testthat/edition": "3", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibglpk-dev make libicu-dev libuv1-dev libxml2-dev libx11-dev", "Repository": "https://markusul.r-universe.dev", "Date/Publication": "2025-12-12 16:40:09 UTC", "RemoteUrl": "https://github.com/markusul/sdmodels", "RemoteRef": "HEAD", "RemoteSha": "c1dd77fba4993f957620093e052f91616cb7f98e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-08 09:04:34 UTC", "User": "root" }, "Author": "Markus Ulmer [aut, cre, cph] (ORCID:\n),\nCyrill 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