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.

Package details

AuthorLotfi Slim [aut, cre], Clément Chatelain [ctb], Chloé-Agathe Azencott [ctb], Jean-Philippe Vert [ctb]
MaintainerLotfi Slim <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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kernelPSI documentation built on June 24, 2019, 5:03 p.m.