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.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | glmnet (≥ 2.0-18), knockoff, spcov, xgboost, Rdpack (≥ 0.11-0), stats, MASS |
| Published: | 2020-02-20 |
| Author: | Tao Jiang [aut, cre] |
| Maintainer: | Tao Jiang <tjiang8 at ncsu.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | KOBT results |
| Reference manual: | KOBT.pdf |
| Package source: | KOBT_0.1.0.tar.gz |
| Windows binaries: | r-devel: KOBT_0.1.0.zip, r-release: KOBT_0.1.0.zip, r-oldrel: KOBT_0.1.0.zip |
| macOS binaries: | r-release: KOBT_0.1.0.tgz, r-oldrel: KOBT_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=KOBT to link to this page.