{
  "_id": "6a103baeacfb0bcc41c9b996",
  "Package": "hdnom",
  "Type": "Package",
  "Title": "Benchmarking and Visualization Toolkit for Penalized Cox Models",
  "Version": "6.2.0",
  "Authors@R": "c(\nperson(\"Nan\", \"Xiao\", email = \"me@nanx.me\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0250-5673\")),\nperson(\"Qing-Song\", \"Xu\", email = \"qsxu@csu.edu.cn\", role = c(\"aut\")),\nperson(\"Miao-Zhu\", \"Li\", email = \"miaozhu.li@duke.edu\", role = c(\"aut\")),\nperson(\"Frank\", \"Harrell\", email = \"f.harrell@vanderbilt.edu\", role = c(\"ctb\"), comment = \"rms author\"),\nperson(\"Sergej\", \"Potapov\", role = c(\"ctb\"), comment = \"survAUC author\"),\nperson(\"Werner\", \"Adler\", role = c(\"ctb\"), comment = \"survAUC author\"),\nperson(\"Matthias\", \"Schmid\", role = c(\"ctb\"), comment = \"survAUC author\")\n)",
  "Description": "Creates nomogram visualizations for penalized Cox\nregression models, with the support of reproducible survival\nmodel building, validation, calibration, and comparison for\nhigh-dimensional data.",
  "License": "GPL (>= 3)",
  "LazyData": "TRUE",
  "URL": "https://nanx.me/hdnom/, https://github.com/nanxstats/hdnom",
  "BugReports": "https://github.com/nanxstats/hdnom/issues",
  "VignetteBuilder": "knitr",
  "Encoding": "UTF-8",
  "Config/roxygen2/version": "8.0.0",
  "Repository": "https://nanxstats.r-universe.dev",
  "Date/Publication": "2026-05-11 07:37:05 UTC",
  "RemoteUrl": "https://github.com/nanxstats/hdnom",
  "RemoteRef": "HEAD",
  "RemoteSha": "c21297cc372208d129dd2121b1dc85ee12fb99df",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-11 10:38:51 UTC",
    "User": "root"
  },
  "Author": "Nan Xiao [aut, cre] (ORCID: <https://orcid.org/0000-0002-0250-5673>),\nQing-Song Xu [aut],\nMiao-Zhu Li [aut],\nFrank Harrell [ctb] (rms author),\nSergej Potapov [ctb] (survAUC author),\nWerner Adler [ctb] (survAUC author),\nMatthias Schmid [ctb] (survAUC author)",
  "Maintainer": "Nan Xiao <me@nanx.me>",
  "MD5sum": "e062f5824b1cd850de3d106d4356de8c",
  "_user": "nanxstats",
  "_type": "src",
  "_file": "hdnom_6.2.0.tar.gz",
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  "_created": "2026-05-11T10:38:51.000Z",
  "_published": "2026-05-22T11:19:10.738Z",
  "_distro": "noble",
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    "id": "c21297cc372208d129dd2121b1dc85ee12fb99df",
    "author": "Nan Xiao <me@nanx.me>",
    "committer": "GitHub <noreply@github.com>",
    "message": "Merge pull request #27 from nanxstats/release\n\nRelease hdnom 6.2.0",
    "time": 1778485025
  },
  "_maintainer": {
    "name": "Nan Xiao",
    "email": "me@nanx.me",
    "login": "nanxstats",
    "bluesky": "@nanxstats.bsky.social",
    "linkedin": "in/nanxstats",
    "twitter": "@nanxstats",
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  "_dependencies": [
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      "package": "R",
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      "package": "foreach",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
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    },
    {
      "package": "glmnet",
      "role": "Imports"
    },
    {
      "package": "gridExtra",
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    },
    {
      "package": "ncvreg",
      "role": "Imports"
    },
    {
      "package": "penalized",
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    },
    {
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    },
    {
      "package": "doParallel",
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  ],
  "_tags": [
    {
      "name": "v6.1.0",
      "date": "2025-06-09"
    }
  ],
  "_topics": [
    "benchmark",
    "high-dimensional-data",
    "linear-regression",
    "nomogram-visualization",
    "penalized-cox-models",
    "survival-analysis",
    "openblas"
  ],
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  "_userbio": {
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    "type": "user",
    "name": "Nan Xiao",
    "description": "Data Scientist @Genentech"
  },
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    "count": 374,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/hdnom"
  },
  "_mentions": 9,
  "_devurl": "https://github.com/nanxstats/hdnom",
  "_pkgdown": "https://nanx.me/hdnom/",
  "_searchresults": 71,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/hdnom.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/nanxstats/hdnom",
  "_realowner": "nanxstats",
  "_cranurl": true,
  "_releases": [
    {
      "version": "1.2",
      "date": "2015-08-27"
    },
    {
      "version": "2.0",
      "date": "2015-09-16"
    },
    {
      "version": "2.1",
      "date": "2015-10-27"
    },
    {
      "version": "3.0",
      "date": "2016-01-04"
    },
    {
      "version": "3.6",
      "date": "2016-03-24"
    },
    {
      "version": "4.0",
      "date": "2016-07-05"
    },
    {
      "version": "4.1",
      "date": "2016-08-05"
    },
    {
      "version": "4.2",
      "date": "2016-10-22"
    },
    {
      "version": "4.5",
      "date": "2016-12-25"
    },
    {
      "version": "4.6",
      "date": "2017-01-08"
    },
    {
      "version": "4.8",
      "date": "2017-03-25"
    },
    {
      "version": "4.9",
      "date": "2017-09-29"
    },
    {
      "version": "5.0",
      "date": "2018-05-14"
    },
    {
      "version": "6.0.0",
      "date": "2019-06-23"
    },
    {
      "version": "6.0.1",
      "date": "2022-05-18"
    },
    {
      "version": "6.0.2",
      "date": "2023-04-24"
    },
    {
      "version": "6.0.3",
      "date": "2024-03-03"
    },
    {
      "version": "6.0.4",
      "date": "2024-09-05"
    },
    {
      "version": "6.1.0",
      "date": "2025-06-09"
    }
  ],
  "_exports": [
    "as_nomogram",
    "calibrate",
    "calibrate_external",
    "compare_by_calibrate",
    "compare_by_validate",
    "fit_aenet",
    "fit_alasso",
    "fit_enet",
    "fit_flasso",
    "fit_lasso",
    "fit_mcp",
    "fit_mnet",
    "fit_scad",
    "fit_snet",
    "glmnet_basesurv",
    "glmnet_survcurve",
    "infer_variable_type",
    "kmplot",
    "logrank_test",
    "ncvreg_basesurv",
    "ncvreg_survcurve",
    "penalized_basesurv",
    "penalized_survcurve",
    "theme_hdnom",
    "validate",
    "validate_external"
  ],
  "_datasets": [
    {
      "name": "smart",
      "title": "Imputed SMART study data",
      "object": "smart",
      "class": [
        "data.frame"
      ],
      "fields": [
        "TEVENT",
        "EVENT",
        "SEX",
        "AGE",
        "DIABETES",
        "CEREBRAL",
        "CARDIAC",
        "AAA",
        "PERIPH",
        "STENOSIS",
        "SYSTBP",
        "DIASTBP",
        "SYSTH",
        "DIASTH",
        "LENGTH",
        "WEIGHT",
        "BMI",
        "CHOL",
        "HDL",
        "LDL",
        "TRIG",
        "HOMOC",
        "GLUT",
        "CREAT",
        "IMT",
        "ALBUMIN",
        "SMOKING",
        "PACKYRS",
        "ALCOHOL"
      ],
      "rows": 3873,
      "table": true,
      "tojson": false
    },
    {
      "name": "smarto",
      "title": "Original SMART study data",
      "object": "smarto",
      "class": [
        "data.frame"
      ],
      "fields": [
        "TEVENT",
        "EVENT",
        "SEX",
        "AGE",
        "DIABETES",
        "CEREBRAL",
        "CARDIAC",
        "AAA",
        "PERIPH",
        "STENOSIS",
        "SYSTBP",
        "DIASTBP",
        "SYSTH",
        "DIASTH",
        "LENGTH",
        "WEIGHT",
        "BMI",
        "CHOL",
        "HDL",
        "LDL",
        "TRIG",
        "HOMOC",
        "GLUT",
        "CREAT",
        "IMT",
        "ALBUMIN",
        "SMOKING",
        "PACKYRS",
        "ALCOHOL"
      ],
      "rows": 3873,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as_nomogram",
      "title": "Construct nomogram ojects for high-dimensional Cox models",
      "topics": [
        "as_nomogram"
      ]
    },
    {
      "page": "calibrate",
      "title": "Calibrate high-dimensional Cox models",
      "topics": [
        "calibrate"
      ]
    },
    {
      "page": "calibrate_external",
      "title": "Externally calibrate high-dimensional Cox models",
      "topics": [
        "calibrate_external"
      ]
    },
    {
      "page": "compare_by_calibrate",
      "title": "Compare high-dimensional Cox models by model calibration",
      "topics": [
        "compare_by_calibrate"
      ]
    },
    {
      "page": "compare_by_validate",
      "title": "Compare high-dimensional Cox models by model validation",
      "topics": [
        "compare_by_validate"
      ]
    },
    {
      "page": "fit_aenet",
      "title": "Model selection for high-dimensional Cox models with adaptive elastic-net penalty",
      "topics": [
        "fit_aenet"
      ]
    },
    {
      "page": "fit_alasso",
      "title": "Model selection for high-dimensional Cox models with adaptive lasso penalty",
      "topics": [
        "fit_alasso"
      ]
    },
    {
      "page": "fit_enet",
      "title": "Model selection for high-dimensional Cox models with elastic-net penalty",
      "topics": [
        "fit_enet"
      ]
    },
    {
      "page": "fit_flasso",
      "title": "Model selection for high-dimensional Cox models with fused lasso penalty",
      "topics": [
        "fit_flasso"
      ]
    },
    {
      "page": "fit_lasso",
      "title": "Model selection for high-dimensional Cox models with lasso penalty",
      "topics": [
        "fit_lasso"
      ]
    },
    {
      "page": "fit_mcp",
      "title": "Model selection for high-dimensional Cox models with MCP penalty",
      "topics": [
        "fit_mcp"
      ]
    },
    {
      "page": "fit_mnet",
      "title": "Model selection for high-dimensional Cox models with Mnet penalty",
      "topics": [
        "fit_mnet"
      ]
    },
    {
      "page": "fit_scad",
      "title": "Model selection for high-dimensional Cox models with SCAD penalty",
      "topics": [
        "fit_scad"
      ]
    },
    {
      "page": "fit_snet",
      "title": "Model selection for high-dimensional Cox models with Snet penalty",
      "topics": [
        "fit_snet"
      ]
    },
    {
      "page": "glmnet_basesurv",
      "title": "Breslow baseline hazard estimator for glmnet objects",
      "topics": [
        "glmnet_basesurv"
      ]
    },
    {
      "page": "glmnet_survcurve",
      "title": "Survival curve prediction for glmnet objects",
      "topics": [
        "glmnet_survcurve"
      ]
    },
    {
      "page": "infer_variable_type",
      "title": "Extract information of selected variables from high-dimensional Cox models",
      "topics": [
        "infer_variable_type"
      ]
    },
    {
      "page": "kmplot",
      "title": "Kaplan-Meier plot with number at risk table for internal calibration and external calibration results",
      "topics": [
        "kmplot"
      ]
    },
    {
      "page": "logrank_test",
      "title": "Log-rank test for internal calibration and external calibration results",
      "topics": [
        "logrank_test"
      ]
    },
    {
      "page": "ncvreg_basesurv",
      "title": "Breslow baseline hazard estimator for ncvreg objects",
      "topics": [
        "ncvreg_basesurv"
      ]
    },
    {
      "page": "ncvreg_survcurve",
      "title": "Survival curve prediction for ncvreg objects",
      "topics": [
        "ncvreg_survcurve"
      ]
    },
    {
      "page": "penalized_basesurv",
      "title": "Breslow baseline hazard estimator for penfit objects",
      "topics": [
        "penalized_basesurv"
      ]
    },
    {
      "page": "penalized_survcurve",
      "title": "Survival curve prediction for penfit objects",
      "topics": [
        "penalized_survcurve"
      ]
    },
    {
      "page": "plot.hdnom.calibrate",
      "title": "Plot calibration results",
      "topics": [
        "plot.hdnom.calibrate"
      ]
    },
    {
      "page": "plot.hdnom.calibrate.external",
      "title": "Plot external calibration results",
      "topics": [
        "plot.hdnom.calibrate.external"
      ]
    },
    {
      "page": "plot.hdnom.compare.calibrate",
      "title": "Plot model comparison by calibration results",
      "topics": [
        "plot.hdnom.compare.calibrate"
      ]
    },
    {
      "page": "plot.hdnom.compare.validate",
      "title": "Plot model comparison by validation results",
      "topics": [
        "plot.hdnom.compare.validate"
      ]
    },
    {
      "page": "plot.hdnom.nomogram",
      "title": "Plot nomogram objects",
      "topics": [
        "plot.hdnom.nomogram"
      ]
    },
    {
      "page": "plot.hdnom.validate",
      "title": "Plot optimism-corrected time-dependent discrimination curves for validation",
      "topics": [
        "plot.hdnom.validate"
      ]
    },
    {
      "page": "plot.hdnom.validate.external",
      "title": "Plot time-dependent discrimination curves for external validation",
      "topics": [
        "plot.hdnom.validate.external"
      ]
    },
    {
      "page": "predict.hdnom.model",
      "title": "Make predictions from high-dimensional Cox models",
      "topics": [
        "predict.hdnom.model"
      ]
    },
    {
      "page": "print.hdnom.calibrate",
      "title": "Print calibration results",
      "topics": [
        "print.hdnom.calibrate"
      ]
    },
    {
      "page": "print.hdnom.calibrate.external",
      "title": "Print external calibration results",
      "topics": [
        "print.hdnom.calibrate.external"
      ]
    },
    {
      "page": "print.hdnom.compare.calibrate",
      "title": "Print model comparison by calibration results",
      "topics": [
        "print.hdnom.compare.calibrate"
      ]
    },
    {
      "page": "print.hdnom.compare.validate",
      "title": "Print model comparison by validation results",
      "topics": [
        "print.hdnom.compare.validate"
      ]
    },
    {
      "page": "print.hdnom.model",
      "title": "Print high-dimensional Cox model objects",
      "topics": [
        "print.hdnom.model"
      ]
    },
    {
      "page": "print.hdnom.nomogram",
      "title": "Print nomograms objects",
      "topics": [
        "print.hdnom.nomogram"
      ]
    },
    {
      "page": "print.hdnom.validate",
      "title": "Print validation results",
      "topics": [
        "print.hdnom.validate"
      ]
    },
    {
      "page": "print.hdnom.validate.external",
      "title": "Print external validation results",
      "topics": [
        "print.hdnom.validate.external"
      ]
    },
    {
      "page": "smart",
      "title": "Imputed SMART study data",
      "topics": [
        "smart"
      ]
    },
    {
      "page": "smarto",
      "title": "Original SMART study data",
      "topics": [
        "smarto"
      ]
    },
    {
      "page": "summary.hdnom.calibrate",
      "title": "Summary of calibration results",
      "topics": [
        "summary.hdnom.calibrate"
      ]
    },
    {
      "page": "summary.hdnom.calibrate.external",
      "title": "Summary of external calibration results",
      "topics": [
        "summary.hdnom.calibrate.external"
      ]
    },
    {
      "page": "summary.hdnom.compare.calibrate",
      "title": "Summary of model comparison by calibration results",
      "topics": [
        "summary.hdnom.compare.calibrate"
      ]
    },
    {
      "page": "summary.hdnom.compare.validate",
      "title": "Summary of model comparison by validation results",
      "topics": [
        "summary.hdnom.compare.validate"
      ]
    },
    {
      "page": "summary.hdnom.validate",
      "title": "Summary of validation results",
      "topics": [
        "summary.hdnom.validate"
      ]
    },
    {
      "page": "summary.hdnom.validate.external",
      "title": "Summary of external validation results",
      "topics": [
        "summary.hdnom.validate.external"
      ]
    },
    {
      "page": "theme_hdnom",
      "title": "Plot theme (ggplot2) for hdnom",
      "topics": [
        "theme_hdnom"
      ]
    },
    {
      "page": "validate",
      "title": "Validate high-dimensional Cox models with time-dependent AUC",
      "topics": [
        "validate"
      ]
    },
    {
      "page": "validate_external",
      "title": "Externally validate high-dimensional Cox models with time-dependent AUC",
      "topics": [
        "validate_external"
      ]
    }
  ],
  "_pkglogo": "https://github.com/nanxstats/hdnom/raw/HEAD/man/figures/logo.png",
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