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:

Reference manual:	PUlasso.pdf
Vignettes:	PUlasso-vignette

Downloads:

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

Linking:

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