Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Äevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Äevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.48550/arXiv.2502.03969>).
Version: 1.0.13 Imports: data.tree, DiagrammeR, doParallel, future.apply, future, ggplot2, GPUmatrix, gridExtra, locatexec, parallel, pbapply, Rdpack, tidyr, fda, grplasso, rlang Suggests: plotly, datasets, rpart, knitr, rmarkdown, ranger, HDclassif, qpdf, igraph, testthat (≥ 3.0.0) Published: 2025-06-05 DOI: 10.32614/CRAN.package.SDModels Author: Markus Ulmer [aut, cre, cph], Cyrill Scheidegger [aut] Maintainer: Markus Ulmer <markus.ulmer at stat.math.ethz.ch> BugReports: https://github.com/markusul/SDModels/issues License: GPL-3 URL: https://www.markus-ulmer.ch/SDModels/ NeedsCompilation: no Citation: SDModels citation info Materials: README NEWS CRAN checks: SDModels results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SDModels to link to this page.
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