{ "_id": "6a23c957530b9bc726be0f2e", "Package": "abess", "Type": "Package", "Title": "Fast Best Subset Selection", "Version": "0.4.11", "Date": "2025-12-07", "Authors@R": "c(\nperson(given = \"Jin\", family = \"Zhu\", email = \"zhuj1jqx@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-8550-5822\")),\nperson(given = \"Zezhi\", family = \"Wang\", email = \"homura@mail.ustc.edu.cn\", role = c(\"aut\")),\nperson(given = \"Liyuan\", family = \"Hu\", email = \"huly5@mail2.sysu.edu.cn\", role = c(\"aut\")),\nperson(given = \"Junhao\", family = \"Huang\", email = \"huangjh256@mail2.sysu.edu.cn\", role = c(\"aut\")),\nperson(given = \"Kangkang\", family = \"Jiang\", email = \"jiangkk3@mail2.sysu.edu.cn\", role = c(\"aut\")),\nperson(given = \"Yanhang\", family = \"Zhang\", email = \"zhangyh98@ruc.edu.cn\", role = c(\"aut\")),\nperson(given = \"Borui\", family = \"Tang\", email = \"tangborui@mail.ustc.edu.cn\", role = c(\"aut\")),\nperson(given = \"Shiyun\", family = \"Lin\", email = \"shiyunlin@stu.pku.edu.cn\", role = c(\"aut\")),\nperson(given = \"Junxian\", family = \"Zhu\", email = \"adaizjx@163.com\", role = c(\"aut\")),\nperson(given = \"Canhong\", family = \"Wen\", email = \"wencanhong@gmail.com\", role = c(\"aut\")),\nperson(given = \"Heping\", family = \"Zhang\", email = \"heping.zhang@yale.edu\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-0688-4076\")),\nperson(given = \"Xueqin\", family = \"Wang\", email = \"wangxq20@ustc.edu.cn\", role = c(\"aut\"), comment = c(ORCID = \"0000-0001-5205-9950\")),\nperson(\"spectra contributors\", role = c(\"cph\"), comment = \"Spectra implementation\")\n)", "Maintainer": "Jin Zhu ", "Description": "Extremely efficient toolkit for solving the best subset\nselection problem\n. This package is\nits R interface. The package implements and generalizes\nalgorithms designed in that\nexploits a novel sequencing-and-splicing technique to guarantee\nexact support recovery and globally optimal solution in\npolynomial times for linear model. It also supports best subset\nselection for logistic regression, Poisson regression, Cox\nproportional hazard model, Gamma regression, multiple-response\nregression, multinomial logistic regression, ordinal\nregression, Ising model reconstruction\n, (sequential) principal\ncomponent analysis, and robust principal component analysis.\nThe other valuable features such as the best subset of group\nselection and sure independence\nscreening are also\nprovided.", "License": "GPL (>= 3) | file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "URL": "https://github.com/abess-team/abess,\nhttps://abess-team.github.io/abess/,\nhttps://abess.readthedocs.io", "BugReports": "https://github.com/abess-team/abess/issues", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-06 07:04:07 UTC", "User": "root" }, "Author": "Jin Zhu [aut, cre] (ORCID:\n), Zezhi Wang [aut],\nLiyuan Hu [aut], Junhao Huang [aut], Kangkang Jiang [aut],\nYanhang Zhang 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