{ "_id": "6ae639ec1e2c7b3a4f52f0e1", "Package": "BJM", "Title": "Backward Joint Model for the Dynamic Prediction of Both\nTime-to-Event and Longitudinal Outcomes", "Version": "0.3.0", "Authors@R": "c(\nperson(\"Wenhao\", \"Li\", email = \"wenhaoli.jlu@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Liang\", \"Li\", role = c('aut'),email = \"LLi15@mdanderson.org\"))", "Maintainer": "Wenhao Li ", "Description": "Provides tools to fit joint models of multivariate\nlongitudinal data and time-to-event data for dynamic\nprediction. It allows the joint prediction of both future\ntime-to-event outcomes and future longitudinal outcomes\nconditional on survival. The models accommodate irregularly\nmeasured longitudinal data and competing risks outcomes. The\nuse of the backward joint model enables fast and efficient\ncomputation, especially for applications with large sample\nsizes and many longitudinal variables. Methods without\ncompeting risks are described in Shen and Li (2021)\n, and their extension to competing risks\nis described in Li et al. (2026)\n.", "License": "MIT + file LICENSE", "URL": "https://wenhaoli18.github.io/BJM/,\nhttps://github.com/wenhaoli18/BJM", "BugReports": "https://github.com/wenhaoli18/BJM/issues", "LazyData": "true", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-31 18:49:44 UTC", "User": "root" }, "Author": "Wenhao Li [aut, cre], Liang Li [aut]", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-10-31 12:48:18 UTC", "RemoteUrl": "https://github.com/cran/BJM", "RemoteRef": "HEAD", "RemoteSha": "0fc5e1fa7e43f96709456c2078e209659f00ad0d", "_user": "cran", "_type": "src", "_file": "BJM_0.3.0.tar.gz", "_fileid": "https://r2.ropensci.org/ef9f43f1f958bae6f1a321a159e4378dc8fa27cdb9ade3e27277a3a32533360c", "_filesize": 897440, "_sha256": "ef9f43f1f958bae6f1a321a159e4378dc8fa27cdb9ade3e27277a3a32533360c", "_expires": "2027-02-08T18:56:42.000Z", "_created": 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