High-Dimensional Variable Selection with Presence-Only Data R package PUlasso version 3.2.6 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>. Software Depends: R(>= 2.10) Imports: Rcpp (>= 0.12.8), methods, Matrix, doParallel, foreach, ggplot2 LinkingTo: Rcpp, RcppEigen, Matrix Suggests: testthat, knitr, rmarkdown Hyebin Song <hps5320@psu.edu> Comprehensive R Archive Network (CRAN) Hyebin Song [aut, cre], Garvesh Raskutti [aut] GPL-2 2026-02-13 application/tgz https://CRAN.R-project.org/package=PUlasso doi:10.32614/CRAN.package.PUlasso