{ "_id": "6a1080a0acfb0bcc41cae937", "Package": "evalITR", "Title": "Evaluating Individualized Treatment Rules", "Version": "1.0.0", "Date": "2023-08-20", "Authors@R": "c(\nperson(\"Michael Lingzhi\", \"Li\", , \"mili@hbs.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Kosuke\", \"Imai\", , \"imai@harvard.edu\", role = \"aut\"),\nperson(\"Jialu\", \"Li\", , \"jialu_li@g.harvard.edu\", role = \"ctb\"),\nperson(\"Xiaolong\", \"Yang\", , \"xiaolong_yang@g.harvard.edu\", role = \"ctb\")\n)", "Maintainer": "Michael Lingzhi Li ", "Description": "Provides various statistical methods for evaluating\nIndividualized Treatment Rules under randomized data. The\nprovided metrics include Population Average Value (PAV),\nPopulation Average Prescription Effect (PAPE), Area Under\nPrescription Effect Curve (AUPEC). It also provides the tools\nto analyze Individualized Treatment Rules under budget\nconstraints. Detailed reference in Imai and Li (2019)\n.", "License": "GPL (>=2)", "URL": "https://github.com/MichaelLLi/evalITR,\nhttps://michaellli.github.io/evalITR/,\nhttps://jialul.github.io/causal-ml/", "BugReports": "https://github.com/MichaelLLi/evalITR/issues", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.2", "Language": "en-US", "Config/pak/sysreqs": "make libicu-dev libx11-dev zlib1g-dev", "Repository": "https://michaellli.r-universe.dev", "Date/Publication": "2023-08-26 03:43:51 UTC", "RemoteUrl": "https://github.com/michaellli/evalitr", "RemoteRef": "HEAD", "RemoteSha": "50d68e9c985738aa7a1cb982545bba3255390482", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-06 06:17:53 UTC", "User": "root" }, "Author": "Michael Lingzhi Li [aut, cre],\nKosuke Imai [aut],\nJialu Li [ctb],\nXiaolong Yang [ctb]", "MD5sum": "53fc7b0e7c56ea031adb6a31189bac3e", "_user": "michaellli", "_type": "src", "_file": "evalITR_1.0.0.tar.gz", "_fileid": 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