kernelPSI (1.1.1)

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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.

Maintainer: Lotfi Slim
Author(s): Lotfi Slim [aut, cre], Clment Chatelain [ctb], Chlo-Agathe Azencott [ctb], Jean-Philippe Vert [ctb]

License: GPL (>= 2)

Uses: CompQuadForm, kernlab, lmtest, pracma, Rcpp, bindata, MASS, testthat, knitr, rmarkdown

Released 8 days ago.

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