# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "SplitKnockoff" in publications use:'
type: software
license: MIT
title: 'SplitKnockoff: Split Knockoffs for Structural Sparsity'
version: '1.1'
doi: 10.32614/CRAN.package.SplitKnockoff
abstract: Split Knockoff is a data adaptive variable selection framework for controlling
  the (directional) false discovery rate (FDR) in structural sparsity, where variable
  selection on linear transformation of parameters is of concern. This proposed scheme
  relaxes the linear subspace constraint to its neighborhood, often known as variable
  splitting in optimization. Simulation experiments can be reproduced following the
  Vignette. We include data (both .mat and .csv format) and application with our method
  of Alzheimer's Disease study in this package. 'Split Knockoffs' is first defined
  in Cao et al. (2021) <arXiv:2103.16159>.
authors:
- family-names: Wang
  given-names: Haoxue
  email: haoxwang@student.ethz.ch
- family-names: Cao
  given-names: Yang
- family-names: Sun
  given-names: Xinwei
- family-names: Yao
  given-names: Yuan
repository: https://llmquantx.r-universe.dev
repository-code: https://github.com/wanghaoxue0/SplitKnockoff
commit: 259761699f55a1b4bf60fddc4c659263812d341f
url: https://github.com/wanghaoxue0/SplitKnockoff
date-released: '2022-02-20'
contact:
- family-names: Wang
  given-names: Haoxue
  email: haoxwang@student.ethz.ch