Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et. al (2007) <doi:10.1093/bioinformatics/btm423> and Meinshausen and Buhlmann (2006) <doi:10.1214/009053606000000281>), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x> and Liu, Roeder and Wasserman (2010) <arXiv:1006.3316> methods. Interactive R app is available.
| Version: | 0.0.6 |
| Depends: | R (≥ 3.6.0), survival, data.table |
| Imports: | Rcpp (≥ 1.0.0), methods, stats, Matrix, ggplot2, gridExtra, maxstat, survminer, plotROC, shiny, foreach, pROC, huge, OptimalCutpoints |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown |
| Published: | 2019-05-10 |
| Author: | Kaiqiao Li [aut, cre], Pei Fen Kuan [aut], Xuefeng Wang [aut] |
| Maintainer: | Kaiqiao Li <kaiqiao.li at stonybrook.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| CRAN checks: | glmaag results |
| Reference manual: | glmaag.pdf |
| Vignettes: |
Vignette Title |
| Package source: | glmaag_0.0.6.tar.gz |
| Windows binaries: | r-devel: glmaag_0.0.6.zip, r-release: glmaag_0.0.6.zip, r-oldrel: glmaag_0.0.6.zip |
| macOS binaries: | r-release: glmaag_0.0.6.tgz, r-oldrel: glmaag_0.0.6.tgz |
| Old sources: | glmaag archive |
Please use the canonical form https://CRAN.R-project.org/package=glmaag to link to this page.