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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) <doi:10.48550/arXiv.1711.08129>.

Versions across snapshots

VersionRepositoryFileSize
3.2.6 rolling linux/jammy R-4.5 PUlasso_3.2.6.tar.gz 593.1 KiB
3.2.6 rolling linux/noble R-4.5 PUlasso_3.2.6.tar.gz 607.6 KiB
3.2.6 rolling source/ R- PUlasso_3.2.6.tar.gz 305.0 KiB
3.2.6 latest linux/jammy R-4.5 PUlasso_3.2.6.tar.gz 593.1 KiB
3.2.6 latest linux/noble R-4.5 PUlasso_3.2.6.tar.gz 607.6 KiB
3.2.6 latest source/ R- PUlasso_3.2.6.tar.gz 305.0 KiB
3.2.6 2026-04-26 source/ R- PUlasso_3.2.6.tar.gz 305.0 KiB
3.2.6 2026-04-23 source/ R- PUlasso_3.2.6.tar.gz 305.0 KiB
3.2.6 2026-04-09 windows/windows R-4.5 PUlasso_3.2.6.zip 920.1 KiB
3.2.5 2025-04-20 source/ R- PUlasso_3.2.5.tar.gz 304.9 KiB

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