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, < http://proceedings.mlr.press/v97/slim19a/slim19a.pdf>. 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.


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install.packages("kernelPSI")

1.1.0 by Lotfi Slim, 7 days ago


http://proceedings.mlr.press/v97/slim19a.html


Browse source code at https://github.com/cran/kernelPSI


Authors: Lotfi Slim [aut, cre] , Clément Chatelain [ctb] , Chloé-Agathe Azencott [ctb] , Jean-Philippe Vert [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp, CompQuadForm, pracma, kernlab, lmtest

Suggests bindata, knitr, rmarkdown, MASS, testthat

Linking to Rcpp, RcppArmadillo


See at CRAN