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 |
Documentation:
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