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) .


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Reference manual

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install.packages("PUlasso")

2.2 by Hyebin Song, 10 days ago


https://arxiv.org/abs/1711.08129


Report a bug at https://github.com/hsong1/PUlasso/issues


Browse source code at https://github.com/cran/PUlasso


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


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp, methods, Matrix

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, BH, RcppEigen, Matrix


See at CRAN