PRIMAL
Parametric Simplex Method for Sparse Learning
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.3 |
rolling linux/jammy R-4.5 | PRIMAL_1.0.3.tar.gz |
460.8 KiB |
1.0.3 |
rolling linux/noble R-4.5 | PRIMAL_1.0.3.tar.gz |
466.4 KiB |
1.0.3 |
rolling source/ R- | PRIMAL_1.0.3.tar.gz |
517.2 KiB |
1.0.3 |
latest linux/jammy R-4.5 | PRIMAL_1.0.3.tar.gz |
460.8 KiB |
1.0.3 |
latest linux/noble R-4.5 | PRIMAL_1.0.3.tar.gz |
466.4 KiB |
1.0.3 |
latest source/ R- | PRIMAL_1.0.3.tar.gz |
517.2 KiB |
1.0.3 |
2026-04-26 source/ R- | PRIMAL_1.0.3.tar.gz |
517.2 KiB |
1.0.3 |
2026-04-23 source/ R- | PRIMAL_1.0.3.tar.gz |
517.2 KiB |
1.0.3 |
2026-04-09 windows/windows R-4.5 | PRIMAL_1.0.3.zip |
790.4 KiB |
1.0.2 |
2025-04-20 source/ R- | PRIMAL_1.0.2.tar.gz |
516.8 KiB |