APML0: Augmented and Penalized Minimization Method L0
Fit linear, logistic 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 performing hard-thresholding or selecting number of non-zeros. 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.
||Matrix (≥ 1.2-10)
||Rcpp (≥ 0.12.12)
||Xiang Li, Shanghong Xie, Donglin Zeng and Yuanjia Wang
||Xiang Li <spiritcoke at gmail.com>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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