Package: PRIMAL
Type: Package
Title: Parametric Simplex Method for Sparse Learning
Version: 1.0.2
Date: 2020-01-21
Author: Zichong Li, Qianli Shen
Maintainer: Zichong Li <zichongli5@gmail.com>
LinkingTo: Rcpp, RcppEigen
Description: 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>.
Imports: Matrix
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2020-01-22 09:22:31 UTC; lizichong
RoxygenNote: 6.1.1
Repository: CRAN
Date/Publication: 2020-01-22 11:10:02 UTC
Built: R 4.4.0; aarch64-apple-darwin20; 2024-04-06 01:50:03 UTC; unix
Archs: PRIMAL.so.dSYM
