{ "_id": "6abb98dd99f7aa8c6aa4693c", "Package": "BayesPET", "Title": "Bayesian Prediction of Event Times for Blinded Randomized\nControlled Trials", "Version": "0.1.0", "Date": "2026-02-04", "Authors@R": "c(\nperson(\"Xinyi\", \"He\", , \"xinyi.he@uth.tmc.edu\", role = c(\"cre\", \"aut\")),\nperson(\"Jingyan\", \"Fu\", , role = \"aut\"),\nperson(\"Ying\", \"Yuan\", , role = c(\"aut\", \"cph\"))\n)", "Description": "Bayesian methods for predicting the calendar time at which\na target number of events is reached in clinical trials. The\nmethodology applies to both blinded and unblinded settings and\njointly models enrollment, event-time, and censoring processes.\nThe package provides tools for trial data simulation, model\nfitting using 'Stan' via the 'rstan' interface, and event time\nprediction under a wide range of trial designs, including\nvarying sample sizes, enrollment patterns, treatment effects,\nand event or censoring time distributions. The package is\nintended to support interim monitoring, operational planning,\nand decision-making in clinical trial development. Methods are\ndescribed in Fu et al. (2025) .", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Biarch": "true", "SystemRequirements": "GNU make", "LazyData": "true", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-09-29 10:37:04 UTC", "User": "root" }, "Author": "Xinyi He [cre, aut], Jingyan Fu [aut], Ying Yuan [aut, cph]", "Maintainer": "Xinyi He ", "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-02-12 08:00:26 UTC", "RemoteUrl": "https://github.com/cran/BayesPET", "RemoteRef": "HEAD", "RemoteSha": "45fb8b3491f6ce9b776a3296140434d1ec0d2c09", "_user": "cran", "_type": "src", "_file": "BayesPET_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/7aea665f8f2a48acbe0de1c24f8b632f919da13635a9c7b7cc0857b9c221dab5", "_filesize": 420240, "_sha256": "7aea665f8f2a48acbe0de1c24f8b632f919da13635a9c7b7cc0857b9c221dab5", "_expires": "2027-01-07T10:54:19.000Z", "_created": "2026-09-29T10:37:04.000Z", "_published": "2026-09-29T10:54:21.774Z", "_jobs": [ { 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