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


Reference manual: PUlasso.pdf
Vignettes: PUlasso-vignette


Package source: PUlasso_3.2.5.tar.gz
Windows binaries: r-devel: PUlasso_3.2.5.zip, r-release: PUlasso_3.2.5.zip, r-oldrel: PUlasso_3.2.5.zip
macOS binaries: r-release (arm64): PUlasso_3.2.5.tgz, r-oldrel (arm64): PUlasso_3.2.5.tgz, r-release (x86_64): PUlasso_3.2.5.tgz, r-oldrel (x86_64): PUlasso_3.2.5.tgz
Old sources: PUlasso archive


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