PUlasso: High-Dimensional Variable Selection with Presence-Only Data

Efficient algorithm for solving PU (Positive and Unlabelled) 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 via 'OpenMP' are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2017) <arXiv:1711.08129>.

Version: 2.2
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.8), methods, Matrix
LinkingTo: Rcpp, BH, RcppEigen, Matrix
Suggests: testthat, knitr, rmarkdown
Published: 2018-02-14
Author: Hyebin Song [aut, cre], Garvesh Raskutti [aut]
Maintainer: Hyebin Song <hsong56 at wisc.edu>
BugReports: https://github.com/hsong1/PUlasso/issues
License: GPL-2
URL: https://arxiv.org/abs/1711.08129
NeedsCompilation: yes
CRAN checks: PUlasso results

Downloads:

Reference manual: PUlasso.pdf
Vignettes: PUlasso-vignette
Package source: PUlasso_2.2.tar.gz
Windows binaries: r-devel: PUlasso_2.2.zip, r-release: PUlasso_2.1.zip, r-oldrel: PUlasso_2.2.zip
OS X El Capitan binaries: r-release: PUlasso_2.2.tgz
OS X Mavericks binaries: r-oldrel: PUlasso_2.1.tgz
Old sources: PUlasso archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=PUlasso to link to this page.