Package: SplitKnockoff
Type: Package
Title: Split Knockoffs for Structural Sparsity
Version: 1.2
Date: 2022-02-20
Author: Haoxue Wang [aut, cre] (Development of the whole packages),
  Yang Cao [aut] (Revison of this package),
  Xinwei Sun [aut] (Original ideas about the package),
  Yuan Yao [aut] (Testing for the package and management of the
    development)
Authors@R: c(person("Haoxue", "Wang", role = c("aut","cre"),
                     comment="Development of the whole packages",
                     email="haoxwang@student.ethz.ch"),
             person("Yang", "Cao", role = c("aut"),
                    comment="Revison of this package"),
             person("Xinwei", "Sun", role = c("aut"),
                    comment="Original ideas about the package"),
             person("Yuan", "Yao", role = c("aut"),
                    comment="Testing for the package and management of the development"))
Maintainer: Haoxue Wang <haoxwang@student.ethz.ch>
Description: 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>. 
URL: https://github.com/wanghaoxue0/SplitKnockoff
BugReports: https://github.com/wanghaoxue0/SplitKnockoff/issues
Depends: R (>= 3.5.0)
Imports: glmnet, MASS, latex2exp, RSpectra, ggplot2, Matrix, stats,
        mvtnorm
Suggests: knitr, rmarkdown
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: no
RoxygenNote: 7.1.2
License: MIT + file LICENSE
Packaged: 2022-03-17 13:55:19 UTC; haoxue
Repository: CRAN
Date/Publication: 2022-03-18 07:40:02 UTC
Built: R 4.2.0; ; 2022-03-22 19:10:38 UTC; unix
