{ "_id": "6a3655d23efcd9bda4401e64", "Package": "APML0", "Type": "Package", "Title": "Augmented and Penalized Minimization Method L0", "Version": "0.11", "Date": "2026-06-13", "Authors@R": "c(\nperson(\"Xiang\", \"Li\", email = \"spiritcoke@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Shanghong\", \"Xie\", role = \"aut\"),\nperson(\"Donglin\", \"Zeng\", role = \"aut\"),\nperson(\"Yuanjia\", \"Wang\", role = \"aut\"))", "Description": "Fit linear, logistic and Cox models regularized with L0,\nlasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian)\npenalty, and their adaptive forms, such as adaptive lasso /\nelastic-net and net adjusting for signs of linked coefficients.\nIt solves the L0 penalty problem by simultaneously selecting\nregularization parameters and performing hard-thresholding or\nselecting the number of non-zeros. This augmented and penalized\nminimization method provides an approximation solution to the\nL0 penalty problem, but runs as fast as L1 regularization. The\npackage uses a one-step coordinate descent algorithm and runs\nextremely fast by taking into account the sparsity structure of\ncoefficients. It can handle very high dimensional data and has\nsuperior selection performance.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "URL": "https://github.com/LeeSprite/APML0", "BugReports": "https://github.com/LeeSprite/APML0/issues", "Config/roxygen2/version": "8.0.0", "Language": "en-US", "Repository": "https://leesprite.r-universe.dev", "Date/Publication": "2026-06-13 15:27:30 UTC", "RemoteUrl": "https://github.com/leesprite/apml0", "RemoteRef": "HEAD", "RemoteSha": "e1495a087a6a61d4f141b2581ceb03e7f456a122", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-20 08:50:37 UTC", "User": "root" }, "Author": "Xiang Li [aut, cre],\nShanghong Xie [aut],\nDonglin Zeng [aut],\nYuanjia Wang [aut]", "Maintainer": "Xiang Li ", "MD5sum": "3fd91ddb6c7c1b279dd4bd09146ad0b1", "_user": "leesprite", "_type": "src", "_file": "APML0_0.11.tar.gz", "_fileid": "5c3198d83374f5d9117207d67f1f34d7636e994c44331cbb23bc756cf43f69f2", "_filesize": 196138, "_sha256": 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