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) <arXiv:1711.08129>.

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Description file content

Package
PUlasso
Type
Package
Title
High-Dimensional Variable Selection with Presence-Only Data
Version
3.2.2
Date
2019-2-27
Description
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) .
License
GPL-2
Imports
Rcpp (>= 0.12.8), methods, Matrix, doParallel, foreach, ggplot2
Depends
R(>= 2.10)
LinkingTo
Rcpp, RcppEigen, Matrix
RoxygenNote
6.0.1
Suggests
testthat, knitr, rmarkdown
VignetteBuilder
knitr
URL
BugReports
https://github.com/hsong1/PUlasso/issues
NeedsCompilation
yes
Packaged
2019-02-28 17:14:27 UTC; Hyebin
Author
Hyebin Song [aut, cre], Garvesh Raskutti [aut]
Maintainer
Hyebin Song
Repository
CRAN
Date/Publication
2019-02-28 17:40:03 UTC

install.packages('PUlasso')

3.2.2

21 days ago

https://arxiv.org/abs/1711.08129

Hyebin Song

GPL-2

Depends on

R(>= 2.10)

Imports

Rcpp (>= 0.12.8), methods, Matrix, doParallel, foreach, ggplot2

Suggests

testthat, knitr, rmarkdown

Discussions