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SDModels  

Spectrally Deconfounded Models
View on CRAN: Click here


Download and install SDModels package within the R console
Install from CRAN:
install.packages("SDModels")

Install from Github:
library("remotes")
install_github("cran/SDModels")

Install by package version:
library("remotes")
install_version("SDModels", "1.0.4")



Attach the package and use:
library("SDModels")
Maintained by
Markus Ulmer
[Scholar Profile | Author Map]
First Published: 2025-02-19
Latest Update: 2025-02-19
Description:
Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, 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>).
How to cite:
Markus Ulmer (2025). SDModels: Spectrally Deconfounded Models. R package version 1.0.4, https://cran.r-project.org/web/packages/SDModels. Accessed 01 Apr. 2025.
Previous versions and publish date:
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Complete documentation for SDModels
Functions, R codes and Examples using the SDModels R package
Full SDModels package functions and examples
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