kernelPSI: Post-Selection Inference for Nonlinear Variable Selection

Different post-selection inference strategies for kernel selection, as described in "kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection", Slim et al., Proceedings of Machine Learning Research, 2019, <>. The strategies rest upon quadratic kernel association scores to measure the association between a given kernel and an outcome of interest. The inference step tests for the joint effect of the selected kernels on the outcome. A fast constrained sampling algorithm is proposed to derive empirical p-values for the test statistics.

Version: 1.1.1
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.1), CompQuadForm, pracma, kernlab, lmtest
LinkingTo: Rcpp, RcppArmadillo
Suggests: bindata, knitr, rmarkdown, MASS, testthat
Published: 2019-12-07
Author: Lotfi Slim [aut, cre], Clément Chatelain [ctb], Chloé-Agathe Azencott [ctb], Jean-Philippe Vert [ctb]
Maintainer: Lotfi Slim <lotfi.slim at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: kernelPSI results


Reference manual: kernelPSI.pdf
Vignettes: kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Package source: kernelPSI_1.1.1.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: kernelPSI_1.1.1.tgz, r-release: kernelPSI_1.1.1.tgz, r-oldrel: kernelPSI_1.1.1.tgz
Old sources: kernelPSI archive


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