PUlasso (3.2.3)

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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) .

Maintainer: Hyebin Song
Author(s): Hyebin Song [aut, cre], Garvesh Raskutti [aut]

License: GPL-2

Uses: doParallel, foreach, ggplot2, Matrix, Rcpp, testthat, knitr, rmarkdown

Released 9 months ago.

6 previous versions



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