PUlasso: High-Dimensional Variable Selection with Presence-Only Data (original) (raw)
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 |
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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 |
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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