{
  "_id": "6acf9312e2d863f662952a93",
  "Package": "landmaRk",
  "Title": "Time-to-Event Landmark Analysis using an Array of Longitudinal\nand Survival Sub-Models",
  "Version": "0.1.4.9000",
  "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, et al.\n(2026) <arXiv:2606.24678>.",
  "License": "GPL (>= 3)",
  "BugReports": "https://github.com/VallejosGroup/landmaRk/issues",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "Config/testthat/edition": "3",
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  "URL": "https://vallejosgroup.github.io/landmaRk/,\nhttps://github.com/VallejosGroup/landmaRk",
  "Language": "en-GB",
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  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev",
  "Repository": "https://vallejosgroup.r-universe.dev",
  "Date/Publication": "2026-10-14 10:22:06 UTC",
  "RemoteUrl": "https://github.com/vallejosgroup/landmaRk",
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  "Packaged": {
    "Date": "2026-10-14 14:22:56 UTC",
    "User": "root"
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  "Author": "Victor Velasco-Pardo [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-7166-1573>),\nNathan Constantine-Cooke [aut] (ORCID:\n<https://orcid.org/0000-0002-4437-8713>),\nCharlie Lees [aut] (ORCID: <https://orcid.org/0000-0002-0732-8215>),\nCatalina Vallejos [aut] (ORCID:\n<https://orcid.org/0000-0003-3638-1960>)",
  "Maintainer": "Victor Velasco-Pardo <vvelasco@ed.ac.uk>",
  "_user": "vallejosgroup",
  "_type": "src",
  "_file": "landmaRk_0.1.4.9000.tar.gz",
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  "_expires": "2027-01-22T14:34:56.000Z",
  "_created": "2026-10-14T14:22:56.000Z",
  "_published": "2026-10-14T14:34:58.375Z",
  "_buildurl": "https://github.com/r-universe/vallejosgroup/actions/runs/38579174624",
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    "author": "Víctor Velasco-Pardo <vvelasco@ed.ac.uk>",
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    "message": "Superlandmarking (#218)\n\n* Superlandmarking code\n\n* Datasets for superlandmarking\n\n* Superlandmarking documentation\n\n* Data\n\n* Add additional fields to the LandmarkAnalysis class for superlandmarking analysis\n\n* Superlandmarking vignette\n\n* Typos in vignette\n\n* Format code\n\n* Potential fix for pull request finding 'Add missing “into” in the superlandmarking description'\n\nCo-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>\n\n* Potential fix for pull request finding 'Clarify the superdataset-to-subjects documentation wording'\n\nCo-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>\n\n* Superlandmarking test\n\n* Potential fix for pull request finding 'Preserve formula environments when adding cluster terms'\n\nCo-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>\n\n* Update R/superlandmarking.R\n\nCo-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>\n\n* Fix plot_hazard_ratio() for models with dynamic covariates\n\n* Format code\n\n---------\n\nCo-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>\nCo-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>",
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    "name": "Victor Velasco-Pardo",
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    "compute_risk_sets",
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    "performance_metrics",
    "plot",
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    "predict_survival",
    "prune",
    "prune_risk_sets",
    "show",
    "split_wide_df",
    "summary"
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      "title": "Chronic myeloid leukaemia data: static covariates and survival outcome",
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      ],
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        "tyears",
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        "learn.dis"
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      "tojson": true
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      "object": "ova",
      "class": [
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      "fields": [
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        "d",
        "Karn",
        "Broders",
        "FIGO",
        "Ascites",
        "Diam",
        "id"
      ],
      "rows": 358,
      "table": true,
      "tojson": true
    }
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      "page": "check_lcmm_convergence",
      "title": "Checks convergence of lcmm models",
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    },
    {
      "page": "cml",
      "title": "Chronic myeloid leukaemia data: static covariates and survival outcome",
      "topics": [
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    },
    {
      "page": "cml_wbc",
      "title": "Chronic myeloid leukaemia data: longitudinal white blood cell count",
      "topics": [
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      ]
    },
    {
      "page": "compute_risk_sets",
      "title": "Compute the list of individuals at risk at landmark times",
      "topics": [
        "compute_risk_sets"
      ]
    },
    {
      "page": "compute_risk_sets-LandmarkAnalysis-method",
      "title": "Compute the list of individuals at risk at landmark times",
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    },
    {
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      "topics": [
        "epileptic"
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    },
    {
      "page": "fit_longitudinal",
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      "topics": [
        "fit_longitudinal"
      ]
    },
    {
      "page": "fit_longitudinal-LandmarkAnalysis-method",
      "title": "Fits the specified longitudinal model for time-varying covariates up to the landmark times",
      "topics": [
        "fit_longitudinal,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "fit_superlandmark",
      "title": "Fits a survival model pooled across several landmark times (superlandmarking)",
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        "fit_superlandmark"
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    },
    {
      "page": "fit_superlandmark-LandmarkAnalysis-method",
      "title": "Fits a survival model pooled across several landmark times (superlandmarking)",
      "topics": [
        "fit_superlandmark,LandmarkAnalysis-method"
      ]
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    {
      "page": "fit_survival",
      "title": "Fits the specified survival model at the landmark times and up to the horizon times specified by the user",
      "topics": [
        "fit_survival"
      ]
    },
    {
      "page": "fit_survival-LandmarkAnalysis-method",
      "title": "Fits the specified survival model at the landmark times and up to the horizon times specified by the user",
      "topics": [
        "fit_survival,LandmarkAnalysis-method"
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    {
      "page": "LandmarkAnalysis",
      "title": "Creates an S4 class for a landmarking analysis",
      "topics": [
        "LandmarkAnalysis"
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    },
    {
      "page": "LandmarkAnalysis-class",
      "title": "S4 class for performing a landmarking analysis",
      "topics": [
        "LandmarkAnalysis-class"
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    {
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      "title": "Advanced ovarian cancer data",
      "topics": [
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    {
      "page": "performance_metrics",
      "title": "Performance metrics",
      "topics": [
        "performance_metrics"
      ]
    },
    {
      "page": "performance_metrics-LandmarkAnalysis-method",
      "title": "Performance metrics",
      "topics": [
        "performance_metrics,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "plot_hazard_ratio",
      "title": "Plot the hazard ratio of a covariate as a function of landmark time",
      "topics": [
        "plot_hazard_ratio"
      ]
    },
    {
      "page": "plot_hazard_ratio-LandmarkAnalysis-method",
      "title": "Plot the hazard ratio of a covariate as a function of landmark time",
      "topics": [
        "plot_hazard_ratio,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "plot-LandmarkAnalysis-method",
      "title": "Plot longitudinal observations and predicted survival curve for one individual",
      "topics": [
        "plot,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "predict_longitudinal",
      "title": "Make predictions for time-varying covariates at specified landmark times",
      "topics": [
        "predict_longitudinal"
      ]
    },
    {
      "page": "predict_longitudinal-LandmarkAnalysis-method",
      "title": "Make predictions for time-varying covariates at specified landmark times",
      "topics": [
        "predict_longitudinal,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "predict_superlandmark",
      "title": "Make predictions from a superlandmark survival model",
      "topics": [
        "predict_superlandmark"
      ]
    },
    {
      "page": "predict_superlandmark-LandmarkAnalysis-method",
      "title": "Make predictions from a superlandmark survival model",
      "topics": [
        "predict_superlandmark,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "predict_survival",
      "title": "Make predictions for time-to-event outcomes at specified horizon times",
      "topics": [
        "predict_survival"
      ]
    },
    {
      "page": "predict_survival-LandmarkAnalysis-method",
      "title": "Make predictions for time-to-event outcomes at specified horizon times",
      "topics": [
        "predict_survival,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "prune",
      "title": "Prunes a landmark time from a 'LandmarkAnalysis', removing the risk set, longitudinal submodel and survival submodel from the object.",
      "topics": [
        "prune"
      ]
    },
    {
      "page": "prune_risk_sets",
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      "topics": [
        "prune_risk_sets"
      ]
    },
    {
      "page": "prune_risk_sets-LandmarkAnalysis-method",
      "title": "Prune a set of individuals from a risk set",
      "topics": [
        "prune_risk_sets,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "prune-LandmarkAnalysis-method",
      "title": "Prunes a landmark time from a 'LandmarkAnalysis', removing the risk set, longitudinal submodel and survival submodel from the object.",
      "topics": [
        "prune,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "show-LandmarkAnalysis-method",
      "title": "Displays an object of class \"'LandmarkAnalysis'\"",
      "topics": [
        "show,LandmarkAnalysis-method"
      ]
    },
    {
      "page": "split_wide_df",
      "title": "Split a wide dataframe containing static and dynamic covariates and splits in into a dataframe with the static covariates and a list of dataframes, each associated to a dynamic covariate.",
      "topics": [
        "split_wide_df"
      ]
    },
    {
      "page": "summary-LandmarkAnalysis-method",
      "title": "Summarise a LandmarkAnalysis object",
      "topics": [
        "summary,LandmarkAnalysis-method"
      ]
    }
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