{ "_id": "6a48ee98b5e98c4082a036d6", "Package": "BJM", "Title": "Backward Joint Model for the Dynamic Prediction of Both\nTime-to-Event and Longitudinal Outcomes", "Version": "0.1.0", "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.", "License": "MIT + file LICENSE", "LazyData": "true", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Author": "Wenhao Li [aut, cre], Liang Li [aut]", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-04 11:26:27 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-04 07:50:02 UTC", "RemoteUrl": "https://github.com/cran/BJM", "RemoteRef": "HEAD", "RemoteSha": "d5c44f3c6ca7c4580914fd5958ac6b6998f4fca0", "_user": "cran", "_type": "src", "_file": "BJM_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/e450bc1dde2b553ab6903157aa6becf7a2a250537f28c7c1721f0ac9fcdd6627", "_filesize": 291058, "_sha256": "e450bc1dde2b553ab6903157aa6becf7a2a250537f28c7c1721f0ac9fcdd6627", "_expires": "2026-10-12T11:29:27.000Z", "_created": "2026-07-04T11:26:27.000Z", "_published": "2026-07-04T11:29:28.879Z", "_jobs": [ { "job": 85128437516, "time": 141, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8081272982" }, { "job": 85128437521, "time": 135, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8081272456" }, { "job": 85128277467, "time": 167, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8081257917" }, { "job": 85128437518, "time": 101, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8081268833" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/cran/actions/runs/28704645792", "_status": "success", "_upstream": "https://github.com/cran/BJM", "_commit": { "id": "d5c44f3c6ca7c4580914fd5958ac6b6998f4fca0", "author": "Wenhao Li ", "committer": "cran-robot ", "message": "version 0.1.0\n", "time": 1783151402 }, "_maintainer": { "name": "Wenhao Li", "email": "wenhaoli.jlu@gmail.com" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "survival", "role": "Depends" }, { "package": "nlme", "role": "Imports" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "Matrix", "role": "Imports" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-27", "n": 1 } ], "_tags": [ { "name": "0.1.0", "date": "2026-07-04" } ], "_stars": 0, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "followers": 615, "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BJM" }, "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/BJM.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "LICENSE", "manual.pdf" ], "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2026-07-04" } ], "_exports": [ "cmtPlot", "dynamicPrediction", "dynamicPredictionBio", "longitudinalSub", "predictPlot", "print_BJM", "print_dynamicPrediction", "print_dynamicPredictionBio", "print_longitudinalSub", "print_survivalSub", "riskPlot", "survivalSub" ], "_datasets": [ { "name": "pbc2", "title": "Mayo Clinic primary biliary cirrhosis data", "object": "pbc2", "class": [ "data.frame" ], "fields": [ "id", "years", "status", "drug", "age", "sex", "year", "ascites", "hepatomegaly", "spiders", "edema", "serBilir", "serChol", "albumin", "alkaline", "SGOT", "platelets", "prothrombin", "histologic", "status2" ], "rows": 1945, "table": true, "tojson": true }, { "name": "pbc3", "title": "Mayo Clinic primary biliary cirrhosis data used as example code", "object": "pbc3", "class": [ "data.frame" ], "fields": [ "id", "years", "status", "drug", "age", "sex", "year", "ascites", "hepatomegaly", "spiders", "edema", "serBilir", "serChol", "albumin", "alkaline", "SGOT", "platelets", "prothrombin", "histologic", "status2", "status3", "status4", "status5", "Tyears1", "Tyears2", "Tyears3", "Tyears4" ], "rows": 1945, "table": true, "tojson": true } ], "_help": [ { "page": "cmtPlot", "title": "Plot conditional mean trajectories (CMT)", "topics": [ "cmtPlot" ] }, { "page": "dynamicPrediction", "title": "Dynamic prediction function", "topics": [ "dynamicPrediction" ] }, { "page": "dynamicPredictionBio", "title": "Dynamic prediction function for future biomarker", "topics": [ "dynamicPredictionBio" ] }, { "page": "longitudinalSub", "title": "The process involves estimating parameters for a multivariate linear mixed-effects model, which simultaneously analyzes multiple dependent variables that may be correlated. 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