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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.1.1 by Lotfi Slim, a year ago

Browse source code at

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