Package: KOBT
Type: Package
Title: Knockoff Boosted Tree
Version: 0.1.0
Authors@R: c(person("Tao", "Jiang", role = c("aut", "cre"),
                     email = "tjiang8@ncsu.edu"))
Description: A novel strategy for conducting variable selection without prior model topology 
             knowledge using the knockoff method (Barber and Candes (2015) <doi:10.1214/15-AOS1337>)
             with extreme boosted tree models (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>). 
             This method is inspired by the original knockoff method, where the differences between 
             original and knockoff variables are used for variable selection with false discovery rate 
             control. In addition to the original knockoff generating methods, two new sampling methods 
             are available to be implemented, namely the sparse covariance and principal component 
             knockoff methods. As results, the indices of selected variables are returned.
Depends: R (>= 3.4.0)
Imports: glmnet (>= 2.0-18), knockoff, spcov, xgboost, Rdpack (>=
        0.11-0), stats, MASS
RdMacros: Rdpack
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-11 17:42:46 UTC; tjiang8
Author: Tao Jiang [aut, cre]
Maintainer: Tao Jiang <tjiang8@ncsu.edu>
Repository: CRAN
Date/Publication: 2020-02-20 14:00:10 UTC
Built: R 4.0.2; ; 2020-07-16 19:37:24 UTC; unix
