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) .

PUlasso

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.

Also available on CRAN: https://cran.r-project.org/web/packages/PUlasso/index.html