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Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.

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conda 1022.2 kB | win-64/r-flare-1.6.0.2-r36hda5aaf8_0.tar.bz2  5 years and 11 months ago 1355 main cf202003
conda 1022.0 kB | win-64/r-flare-1.6.0.2-r35hda5aaf8_0.tar.bz2  5 years and 11 months ago 1367 main cf202003
conda 1007.1 kB | osx-64/r-flare-1.6.0.2-r36h159158b_0.tar.bz2  5 years and 11 months ago 357 main cf202003
conda 1009.9 kB | osx-64/r-flare-1.6.0.2-r35h159158b_0.tar.bz2  5 years and 11 months ago 373 main cf202003
conda 1001.9 kB | linux-64/r-flare-1.6.0.2-r35hcdcec82_0.tar.bz2  5 years and 11 months ago 4151 main cf202003
conda 1008.1 kB | linux-64/r-flare-1.6.0.2-r36hcdcec82_0.tar.bz2  5 years and 11 months ago 4053 main cf202003

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