DESCRIPTION

Package: kernelPSI
Title: Post-Selection Inference for Nonlinear Variable Selection
Version: 1.1.1
Date: 2019-12-07
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,
    pracma,
    kernlab,
    lmtest
Suggests: 
    bindata,
    knitr,
    rmarkdown,
    MASS,
    testthat
Encoding: UTF-8
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
RoxygenNote: 7.0.2