Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for lasso regularization of Generalized Linear Models (GLM) and square-root lasso to obtain a sparse model with a good compromise between high true positive rate and low false discovery rate. Giacobino et al. (2017) <doi:10.1214/17-EJS1366>. Sardy et al. (2017) <arXiv:1708.02908>.
| Version: | 2.2 |
| Depends: | Matrix, glmnet, lars, flare |
| Published: | 2021-01-19 |
| Author: | Jairo Diaz-Rodriguez [aut, cre, cph], Sylvain Sardy [aut, ths], Caroline Giacobino [aut], Nick Hengartner [aut] |
| Maintainer: | Jairo Diaz-Rodriguez <adjairo at uninorte.edu.co> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | qut results |
| Reference manual: | qut.pdf |
| Package source: | qut_2.2.tar.gz |
| Windows binaries: | r-devel: qut_2.2.zip, r-release: qut_2.2.zip, r-oldrel: qut_2.2.zip |
| macOS binaries: | r-release: qut_2.2.tgz, r-oldrel: qut_2.2.tgz |
| Old sources: | qut archive |
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