{
  "_id": "6a103771acfb0bcc41c9a47b",
  "Package": "ICBioMark",
  "Title": "Data-Driven Design of Targeted Gene Panels for Estimating\nImmunotherapy Biomarkers",
  "Version": "0.1.5",
  "Authors@R": "c(person(given = \"Jacob R.\",\nfamily = \"Bradley\",\nrole = c(\"aut\", \"cre\"),\nemail = \"cobrbradley@gmail.com\",\ncomment = c(ORCID = \"0000-0003-1616-4969\")),\nperson(given = \"Timothy I.\",\nfamily = \"Cannings\",\nrole = c(\"aut\"),\nemail = \"Timothy.cannings@sms.ed.ac.uk\",\ncomment = c(ORCID = \"0000-0002-2111-4168\")))",
  "Description": "Implementation of the methodology proposed in 'Data-driven\ndesign of targeted gene panels for estimating immunotherapy\nbiomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>.\nThis package allows the user to fit generative models of\nmutation from an annotated mutation dataset, and then further\nto produce tunable linear estimators of exome-wide biomarkers.\nIt also contains functions to simulate mutation annotated\nformat (MAF) data, as well as to analyse the output and\nperformance of models.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.1.1",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://cobrbra.r-universe.dev",
  "Date/Publication": "2023-04-24 16:36:19 UTC",
  "RemoteUrl": "https://github.com/cobrbra/icbiomark",
  "RemoteRef": "HEAD",
  "RemoteSha": "e5040a98925cb52a2169575ed6ecef718ba86523",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-12 06:24:17 UTC",
    "User": "root"
  },
  "Author": "Jacob R. Bradley [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-1616-4969>),\nTimothy I. Cannings [aut] (ORCID:\n<https://orcid.org/0000-0002-2111-4168>)",
  "Maintainer": "Jacob R. Bradley <cobrbradley@gmail.com>",
  "MD5sum": "80ba6bec7561e78baccebc4e95ac7e03",
  "_user": "cobrbra",
  "_type": "src",
  "_file": "ICBioMark_0.1.5.tar.gz",
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  "_created": "2026-05-12T06:24:17.000Z",
  "_published": "2026-05-22T11:01:05.513Z",
  "_distro": "noble",
  "_jobs": [
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  "_host": "GitHub-Actions",
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    "email": "cobrbradley@gmail.com",
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      "package": "glmnet",
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    },
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      "package": "Matrix",
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  "_selfowned": true,
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  "_updates": [],
  "_tags": [],
  "_stars": 0,
  "_contributors": [
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      "count": 92,
      "uuid": 44873829
    }
  ],
  "_userbio": {
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    "type": "user",
    "name": "cobrbra"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/ICBioMark"
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  "_devurl": "https://github.com/cobrbra/icbiomark",
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  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
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  "_homeurl": "https://github.com/cobrbra/icbiomark",
  "_realowner": "cobrbra",
  "_cranurl": true,
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      "date": "2021-02-17"
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      "date": "2021-03-05"
    },
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      "date": "2021-03-18"
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      "date": "2021-11-15"
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    "fit_gen_model_uninteract",
    "fit_gen_model_unisamp",
    "generate_maf_data",
    "get_auprc",
    "get_biomarker_from_maf",
    "get_biomarker_tables",
    "get_gen_estimates",
    "get_K",
    "get_mutation_dictionary",
    "get_mutation_tables",
    "get_p",
    "get_panels_from_fit",
    "get_predictions",
    "get_r_squared",
    "get_stats",
    "get_table_from_maf",
    "pred_first_fit",
    "pred_intervals",
    "pred_refit_panel",
    "pred_refit_range",
    "vis_model_fit"
  ],
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      "name": "ensembl_gene_lengths",
      "title": "Gene Lengths from the Ensembl Database",
      "object": "ensembl_gene_lengths",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Hugo_Symbol",
        "max_cds",
        "Chromosome"
      ],
      "rows": 19795,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_first_pred_tmb",
      "title": "First-Fit Predictive Model Fitting on Example Data",
      "object": "example_first_pred_tmb",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "example_gen_model",
      "title": "Generative Model from Simulated Data",
      "object": "example_gen_model",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "example_maf_data",
      "title": "Simulated MAF Data",
      "object": "example_maf_data",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "example_predictions",
      "title": "Example Predictions",
      "object": "example_predictions",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "example_refit_panel",
      "title": "Refitted Predictive Model Fitted on Example Data",
      "object": "example_refit_panel",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "example_refit_range",
      "title": "Refitted Predictive Models Fitted on Example Data",
      "object": "example_refit_range",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "example_tables",
      "title": "Mutation Matrices from Simulated Data",
      "object": "example_tables",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "example_tib_tables",
      "title": "Tumour Indel Burden of Example Train, Validation and Test Data.",
      "object": "example_tib_tables",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "example_tmb_tables",
      "title": "Tumour Mutation Burden of Example Train, Validation and Test Data.",
      "object": "example_tmb_tables",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "nsclc_maf",
      "title": "Non-Small Cell Lung Cancer MAF Data",
      "object": "nsclc_maf",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Tumor_Sample_Barcode",
        "Hugo_Symbol",
        "Variant_Classification",
        "Chromosome",
        "Start_Position",
        "End_Position"
      ],
      "rows": 299855,
      "table": true,
      "tojson": true
    },
    {
      "name": "nsclc_survival",
      "title": "Non-Small Cell Lung Cancer Survival and Clinical Data",
      "object": "nsclc_survival",
      "class": [
        "data.frame"
      ],
      "fields": [
        "CASE_ID",
        "AGE",
        "AGE_AT_SURGERY",
        "CANCER_TYPE",
        "CANCER_TYPE_DETAILED",
        "DAYS_TO_DEATH",
        "DAYS_TO_LAST_FOLLOWUP",
        "FRACTION_GENOME_ALTERED",
        "HISTORY_NEOADJUVANT_TRTYN",
        "HISTORY_OTHER_MALIGNANCY",
        "MUTATION_COUNT",
        "M_STAGE",
        "N_STAGE",
        "ONCOTREE_CODE",
        "OS_MONTHS",
        "OS_STATUS",
        "SAMPLE_COUNT",
        "SEX",
        "SMOKING_HISTORY",
        "SMOKING_PACK_YEARS",
        "SOMATIC_STATUS",
        "STAGE",
        "T_STAGE"
      ],
      "rows": 1144,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "ensembl_gene_lengths",
      "title": "Gene Lengths from the Ensembl Database",
      "topics": [
        "ensembl_gene_lengths"
      ]
    },
    {
      "page": "example_first_pred_tmb",
      "title": "First-Fit Predictive Model Fitting on Example Data",
      "topics": [
        "example_first_pred_tmb"
      ]
    },
    {
      "page": "example_gen_model",
      "title": "Generative Model from Simulated Data",
      "topics": [
        "example_gen_model"
      ]
    },
    {
      "page": "example_maf_data",
      "title": "Simulated MAF Data",
      "topics": [
        "example_maf_data"
      ]
    },
    {
      "page": "example_predictions",
      "title": "Example Predictions",
      "topics": [
        "example_predictions"
      ]
    },
    {
      "page": "example_refit_panel",
      "title": "Refitted Predictive Model Fitted on Example Data",
      "topics": [
        "example_refit_panel"
      ]
    },
    {
      "page": "example_refit_range",
      "title": "Refitted Predictive Models Fitted on Example Data",
      "topics": [
        "example_refit_range"
      ]
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    {
      "page": "example_tables",
      "title": "Mutation Matrices from Simulated Data",
      "topics": [
        "example_tables"
      ]
    },
    {
      "page": "example_tib_tables",
      "title": "Tumour Indel Burden of Example Train, Validation and Test Data.",
      "topics": [
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    },
    {
      "page": "example_tmb_tables",
      "title": "Tumour Mutation Burden of Example Train, Validation and Test Data.",
      "topics": [
        "example_tmb_tables"
      ]
    },
    {
      "page": "fit_gen_model",
      "title": "Fit Generative Model",
      "topics": [
        "fit_gen_model"
      ]
    },
    {
      "page": "fit_gen_model_uninteract",
      "title": "Fit Generative Model Without Gene/Variant Type-Specific Interactions",
      "topics": [
        "fit_gen_model_uninteract"
      ]
    },
    {
      "page": "fit_gen_model_unisamp",
      "title": "Fit Generative Model Without Sample-Specific Effects",
      "topics": [
        "fit_gen_model_unisamp"
      ]
    },
    {
      "page": "generate_maf_data",
      "title": "Generate mutation data.",
      "topics": [
        "generate_maf_data"
      ]
    },
    {
      "page": "get_auprc",
      "title": "AUPRC Metrics for Predictions",
      "topics": [
        "get_auprc"
      ]
    },
    {
      "page": "get_biomarker_from_maf",
      "title": "Produce a Table of Biomarker Values from a MAF",
      "topics": [
        "get_biomarker_from_maf"
      ]
    },
    {
      "page": "get_biomarker_tables",
      "title": "Get True Biomarker Values on Training, Validation and Test Sets",
      "topics": [
        "get_biomarker_tables"
      ]
    },
    {
      "page": "get_gen_estimates",
      "title": "Investigate Generative Model Comparisons",
      "topics": [
        "get_gen_estimates"
      ]
    },
    {
      "page": "get_K",
      "title": "Construct Bias Penalisation",
      "topics": [
        "get_K"
      ]
    },
    {
      "page": "get_mutation_dictionary",
      "title": "Group and Filter Mutation Types",
      "topics": [
        "get_mutation_dictionary"
      ]
    },
    {
      "page": "get_mutation_tables",
      "title": "Produce Training, Validation and Test Matrices",
      "topics": [
        "get_mutation_tables"
      ]
    },
    {
      "page": "get_p",
      "title": "Construct Optimisation Parameters.",
      "topics": [
        "get_p"
      ]
    },
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      "page": "get_panels_from_fit",
      "title": "Extract Panel Details from Group Lasso Fit",
      "topics": [
        "get_panels_from_fit"
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    },
    {
      "page": "get_predictions",
      "title": "Produce Predictions on an Unseen Dataset",
      "topics": [
        "get_predictions"
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    {
      "page": "get_r_squared",
      "title": "R Squared Metrics for Predictions",
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      "page": "get_stats",
      "title": "Metrics for Predictive Performance",
      "topics": [
        "get_stats"
      ]
    },
    {
      "page": "get_table_from_maf",
      "title": "Produce a Mutation Matrix from a MAF",
      "topics": [
        "get_table_from_maf"
      ]
    },
    {
      "page": "ICBioMark",
      "title": "ICBioMark: A package for cost-effective design of gene panels to predict exome-wide biomarkers.",
      "topics": [
        "ICBioMark"
      ]
    },
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      "page": "nsclc_maf",
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      "page": "pred_refit_panel",
      "title": "Refitted Predictive Model for a Given Panel",
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      "page": "pred_refit_range",
      "title": "Get Refitted Predictive Models for a First-Fit Range of Panels",
      "topics": [
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      "page": "vis_model_fit",
      "title": "Visualise Generative Model Fit",
      "topics": [
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