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) .


Reference manual

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3.2.3 by Hyebin Song, a year ago


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, doParallel, foreach, ggplot2

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, RcppEigen, Matrix

See at CRAN