PUlasso: High-Dimensional Variable Selection with Presence-Only Data
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <doi:10.48550/arXiv.1711.08129>.
| Version: | 3.2.5 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 0.12.8), methods,Matrix,doParallel,foreach,ggplot2 |
| LinkingTo: | Rcpp,RcppEigen,Matrix |
| Suggests: | testthat,knitr,rmarkdown |
| Published: | 2023-12-18 |
| DOI: | 10.32614/CRAN.package.PUlasso |
| Author: | Hyebin Song [aut, cre], Garvesh Raskutti [aut] |
| Maintainer: | Hyebin Song <hps5320 at psu.edu> |
| BugReports: | https://github.com/hsong1/PUlasso/issues |
| License: | GPL-2 |
| URL: | https://arxiv.org/abs/1711.08129 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | PUlasso results |
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