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, <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.
| 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 mines-paristech.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://proceedings.mlr.press/v97/slim19a.html |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
kernelPSI results |
Documentation:
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