Package: kernelPSI
Title: Post-Selection Inference for Nonlinear Variable Selection
Version: 1.0.0
Date: 2019-06-20
Authors@R: c(person("Lotfi", "Slim", email = "lotfi.slim@mines-paristech.fr", role = c("aut", "cre")),
  person("Clément", "Chatelain", email = "clement.chatelain@sanofi.com", role = "ctb"),
  person("Chloé-Agathe", "Azencott", email = "chloe-agathe.azencott@mines-paristech.fr", role ="ctb"),
  person("Jean-Philippe", "Vert", email = "jpvert@google.com", role ="ctb"))
Description: 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.
URL: http://proceedings.mlr.press/v97/slim19a.html
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.1), CompQuadForm, tmg, pracma, kernlab, lmtest
Suggests: bindata, knitr, rmarkdown, MASS, testthat
Encoding: UTF-8
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-06-22 21:28:24 UTC; lotfislim
Author: Lotfi Slim [aut, cre],
  Clément Chatelain [ctb],
  Chloé-Agathe Azencott [ctb],
  Jean-Philippe Vert [ctb]
Maintainer: Lotfi Slim <lotfi.slim@mines-paristech.fr>
Repository: CRAN
Date/Publication: 2019-06-24 13:20:03 UTC
