Augmented and Penalized Minimization Method L0

Fit linear and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves L0 penalty problem by simultaneously selecting regularization parameters and the number of non-zero coefficients. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization problem. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It could deal with very high dimensional data and has superior selection performance.


Reference manual

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0.2 by Xiang Li, 10 days ago

Browse source code at

Authors: Xiang Li, Shanghong Xie, Donglin Zeng and Yuanjia Wang

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Depends on Matrix

Linking to Rcpp, RcppEigen

See at CRAN