Package: PUlasso 3.2.6

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>.

Authors:Hyebin Song [aut, cre], Garvesh Raskutti [aut]

PUlasso_3.2.6.tar.gz
PUlasso_3.2.6.zip(r-4.7-x86_64)PUlasso_3.2.6.zip(r-4.7-arm64)PUlasso_3.2.6.zip(r-4.6-x86_64)PUlasso_3.2.6.zip(r-4.6-arm64)PUlasso_3.2.6.zip(r-4.5-x86_64)
PUlasso_3.2.6.tgz(r-4.6-x86_64)PUlasso_3.2.6.tgz(r-4.6-arm64)PUlasso_3.2.6.tgz(r-4.5-x86_64)PUlasso_3.2.6.tgz(r-4.5-arm64)
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PUlasso_3.2.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PUlasso/json (API)

# Install 'PUlasso' in R:

install.packages('PUlasso', repos = c('https://hsong1.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hsong1/pulasso/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

simulPU - Simulated PU data

On CRAN:

cpp

5.23 score 7 stars 24 scripts 395 downloads 3 exports 25 dependencies

Last updated from:2484d7652f. Checks:13 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	180
linux-devel-x86_64	NOTE	228
source / vignettes	OK	316
linux-release-arm64	NOTE	222
linux-release-x86_64	NOTE	225
macos-release-arm64	NOTE	265
macos-release-x86_64	NOTE	535
macos-oldrel-arm64	NOTE	260
macos-oldrel-x86_64	NOTE	530
windows-devel-arm64	NOTE	420
windows-devel-x86_64	NOTE	209
windows-release-arm64	NOTE	422
windows-release-x86_64	NOTE	213
windows-oldrel-x86_64	NOTE	204
wasm-release	OK	156

Exports:cv.grpPUlasso deviances grpPUlasso

Dependencies:cli codetools cpp11 doParallel farver foreach ggplot2 glue gtable isoband iterators labeling lattice lifecycle Matrix R6 RColorBrewer Rcpp RcppEigen rlang S7 scales vctrs viridisLite withr

PUlasso-vignette

Introduction | Basic usage of PUlasso package | Group Penalty | Fitting with a sparse matrix | Optimization

Rendered fromPUlasso-vignette.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2017-11-28

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
PUlasso : An efficient algorithm to solve Positive and Unlabeled(PU) problem with lasso or group lasso penalty	PUlasso-package PUlasso
Cross-validation for PUlasso	cv.grpPUlasso
Deviance	deviances
Solve PU problem with lasso or group lasso penalty.	grpPUlasso
simulated PU data	simulPU