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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:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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