{ "_id": "6ace2375a14063b82c3b02d1", "Package": "QuantRegGLasso", "Title": "Adaptively Weighted Group Lasso for Semiparametric Quantile\nRegression Models", "Version": "1.0.1", "Authors@R": "c(person(\ngiven = \"Wen-Ting\",\nfamily = \"Wang\",\nemail = \"egpivo@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-3051-7302\")\n),\nperson(\ngiven = \"Wei-Ying\",\nfamily = \"Wu\",\nemail = \"wuweiying1011@gmail.com\",\nrole = c(\"aut\")\n),\nperson(\ngiven = \"Toshio\",\nfamily = \"Honda\",\nemail = \"t.honda@r.hit-u.ac.jp\",\nrole = c(\"aut\")\n),\nperson(\ngiven = \"Ching-Kang\",\nfamily = \"Ing\",\nemail=\"cking@stat.nthu.edu.tw\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-1362-8246\")\n)\n)", "Description": "Implements an adaptively weighted group Lasso procedure\nfor simultaneous variable selection and structure\nidentification in varying coefficient quantile regression\nmodels and additive quantile regression models with ultra-high\ndimensional covariates. The methodology, grounded in a strong\nsparsity condition, establishes selection consistency under\ncertain weight conditions. To address the challenge of tuning\nparameter selection in practice, a BIC-type criterion named\nhigh-dimensional information criterion (HDIC) is proposed. The\nLasso procedure, guided by HDIC-determined tuning parameters,\nmaintains selection consistency. Theoretical findings are\nstrongly supported by simulation studies. (Toshio Honda,\nChing-Kang Ing, Wei-Ying Wu, 2019, ).", "License": "GPL (>= 2)", "ByteCompile": "true", "BugReports": "https://github.com/egpivo/QuantRegGLasso/issues", "SystemRequirements": "GNU make", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "URL": "https://github.com/egpivo/QuantRegGLasso", "Config/testthat/edition": "3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-13 12:19:51 UTC", "User": "root" }, "Author": "Wen-Ting Wang [aut, cre] (ORCID:\n), Wei-Ying Wu [aut],\nToshio Honda [aut], Ching-Kang Ing [aut] (ORCID:\n)", "Maintainer": "Wen-Ting Wang ", "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-10-06 03:30:02 UTC", "RemoteUrl": "https://github.com/cran/QuantRegGLasso", "RemoteRef": "HEAD", "RemoteSha": "08c5b219bdcdaf6b657505b36cad385827c56b10", "_user": "cran", "_type": "src", "_file": "QuantRegGLasso_1.0.1.tar.gz", "_fileid": 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