{
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  "Package": "highMLR",
  "Title": "Machine Learning Feature Selection for High Dimensional Survival\nData",
  "Version": "1.0.1",
  "Date": "2026-05-23",
  "Authors@R": "person(\"Atanu\", \"Bhattacharjee\", email = \"atanustat@gmail.com\",\nrole = c(\"aut\", \"cre\"))",
  "Description": "A unified, flexible framework for high dimensional feature\nselection in the presence of a survival outcome. Provides\nmultiple machine learning approaches (Cox elastic net, random\nsurvival forest, accelerated oblique random survival forest,\ngradient-boosted Cox, stability selection, classical univariate\nCox screening, pseudo- observation bridging to arbitrary\nregression learners, and Fine-Gray competing risks selection)\nunder a single interface. Adds causal survival forest\nestimation of heterogeneous treatment effects on survival\n(experimental), conformal survival prediction with finite-\nsample coverage guarantees, and time-dependent 'SHAP'\nexplanations via 'SurvSHAP(t)'. Methodology is based on\nregularised Cox regression (2011) <doi:10.18637/jss.v039.i05>,\nrandom survival forests (2008) <doi:10.1214/08-AOAS169>,\noblique random survival forests (2024)\n<doi:10.1080/10618600.2023.2231048>, stability selection (2010)\n<doi:10.1111/j.1467-9868.2010.00740.x>, causal survival forests\n(2023) <doi:10.1111/rssb.12538>, time-dependent survival\nexplanations (2023) <doi:10.1016/j.knosys.2022.110234>,\nconformal survival prediction (2023)\n<doi:10.1093/biomet/asad043>, the Fine-Gray model for competing\nrisks (1999) <doi:10.1080/01621459.1999.10474144>, and\npseudo-observation regression (2010)\n<doi:10.1177/0962280209105020>.",
  "License": "GPL-3",
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  "Author": "Atanu Bhattacharjee [aut, cre]",
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  "Repository": "https://atanubhattacharjee.r-universe.dev",
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      "page": "coef.highmlr_fit",
      "title": "Coefficients from a highmlr_fit",
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        "coef.highmlr_fit"
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    {
      "page": "highmlr",
      "title": "Machine learning feature selection for high dimensional survival data",
      "topics": [
        "highmlr"
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    {
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      "title": "Causal survival forest for heterogeneous treatment effects (experimental)",
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        "plot.highmlr_causal",
        "print.highmlr_causal"
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      "title": "Time-dependent SHAP explanations for a highmlr_fit (SurvSHAP(t))",
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        "plot.highmlr_explain",
        "print.highmlr_explain"
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      "title": "Pre-screen features when p is very large",
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      "topics": [
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      "topics": [
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      "title": "Plot method for highmlr_conformal objects",
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      "title": "Forest / importance plot for a highmlr_fit",
      "topics": [
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      "title": "Predict from a highmlr_fit",
      "topics": [
        "predict.highmlr_fit"
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      "page": "print.highmlr_conformal",
      "title": "Print method for highmlr_conformal objects",
      "topics": [
        "print.highmlr_conformal"
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    },
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      "page": "print.highmlr_fit",
      "title": "Print method for highmlr_fit",
      "topics": [
        "print.highmlr_fit"
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      "page": "srdata",
      "title": "High dimensional protein gene expression survival data",
      "topics": [
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      "title": "Summary method for highmlr_fit",
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