{ "_id": "6a1706e1acfb0bcc41d924c7", "Package": "landmaRk", "Title": "Time-to-Event Landmark Analysis using an Array of Longitudinal\nand Survival Sub-Models", "Version": "0.1.1.9000", "Authors@R": "c(\nperson(\"Victor\", \"Velasco-Pardo\", , \"vvelasco@ed.ac.uk\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-7166-1573\")),\nperson(\"Nathan\", \"Constantine-Cooke\", , \"nathan.constantine-cooke@ed.ac.uk\",\nrole = \"aut\", comment = c(ORCID = \"0000-0002-4437-8713\")),\nperson(\"Charlie\", \"Lees\", , \"charlie.lees@ed.ac.uk\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-0732-8215\")),\nperson(\"Catalina\", \"Vallejos\", , \"catalina.vallejos@ed.ac.uk\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-3638-1960\"))\n)", "Description": "Provides a modular end-to-end framework for dynamic risk\nprediction based on time-to-event and longitudinal data. This\nallows flexible specifications for the longitudinal and\nsurvival sub-models. The 'landmaRk' package enables\nreproducible benchmarks of different model choices, including\ncross-validation to assess out-of-sample predictive\nperformance. Methods are described in Velasco-Pardo,\nConstantine-Cooke, Lees and Vallejos (2026, manuscript under\npreparation) 'Landmarking with Latent Class Mixed Models for\nDynamic Prediction of Time-to-event Data with Heterogeneous\nBiomarker Trajectories'.", "License": "GPL (>= 3)", "BugReports": "https://github.com/VallejosGroup/landmaRk/issues", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/testthat/parallel": "true", "LazyData": "true", "VignetteBuilder": "knitr", "URL": "https://vallejosgroup.github.io/landmaRk/,\nhttps://github.com/VallejosGroup/landmaRk", "Language": "en-GB", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://nathansam.r-universe.dev", "Date/Publication": "2026-05-27 14:16:14 UTC", "RemoteUrl": "https://github.com/VallejosGroup/landmaRk", "RemoteRef": "HEAD", "RemoteSha": "50390cea31d385fad13f66906e9f9fbf81ad8b0b", "NeedsCompilation": "no", "Packaged": { 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