{ "_id": "6a1d51151d7bb097a0a45d3a", "Package": "Keyboard", "Type": "Package", "Title": "Bayesian Designs for Early Phase Clinical Trials", "Version": "0.1.3", "Author": "Xiaomeng Yuan, Chen Li, Hongying Sun, Li Tang and Haitao Pan", "Maintainer": "Xiaomeng Yuan ", "Description": "We developed a package 'Keyboard' for designing\nsingle-agent, drug-combination, or phase I/II dose-finding\nclinical trials. The 'Keyboard' designs are novel early phase\ntrial designs that can be implemented simply and transparently,\nsimilar to the 3+3 design, but yield excellent performance,\ncomparable to those of more-complicated, model-based designs\n(Yan F, Mandrekar SJ, Yuan Y (2017)\n, Li DH, Whitmore JB, Guo W,\nJi Y. (2017) , Liu S,\nJohnson VE (2016) , Zhou Y,\nLee JJ, Yuan Y (2019) , Pan H, Lin R,\nYuan Y (2020) ). The 'Keyboard'\npackage provides tools for designing, conducting, and analyzing\nsingle-agent, drug-combination, and phase I/II dose-finding\nclinical trials. For more details about how to use this packge,\nplease refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022)\n\"A software tool for both the maximum tolerated dose and the\noptimal biological dose finding trials in early phase designs\".\nManuscript submitted for publication.", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.2.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 09:27:16 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2022-08-11 09:10:17 UTC", "RemoteUrl": "https://github.com/cran/Keyboard", "RemoteRef": "HEAD", "RemoteSha": "75de0db52bce69090223a4c959fe4f58ada0b2f6", "MD5sum": "51db0c7362acde574407593215f4544e", "_user": "cran", "_type": "src", "_file": "Keyboard_0.1.3.tar.gz", "_fileid": "7e46aa35f5b3dacae5a5e54fded5db6e8ca5f9ea8a914cf876a168ec97b032c6", "_filesize": 401092, "_sha256": "7e46aa35f5b3dacae5a5e54fded5db6e8ca5f9ea8a914cf876a168ec97b032c6", "_created": "2026-06-01T09:27:16.000Z", "_published": "2026-06-01T09:29:57.336Z", "_distro": "noble", "_jobs": [ { "job": 78823560034, "time": 118, 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