{
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  "Package": "FrailtyCompRisk",
  "Title": "Competing Risks Models for Multi-Center Survival Data with\nFrailty",
  "Version": "0.1.1",
  "Authors@R": "c(person(\"Benjamin\", \"Delmas\", email = \"benjamin.delmas@ensae.fr\", role = \"aut\"),\nperson(\"Lucas\", \"Ducrot\", email = \"lucas.ducrot@inria.fr\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8020-2897\")),\nperson(\"Sandrine\", \"Katsahian\", email = \"sandrine.katsahian@aphp.fr\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-7261-0671\")),\nperson(given = \"Inria\", role = \"cph\"))",
  "Description": "Implements methods for analyzing competing risks data in\nmulti-center survival studies using frailty models. The\napproach relies on a mixed proportional hazards model for the\nsub-distribution, allowing for cluster-specific random effects.\nThe package provides tools for model estimation with or without\nfrailty using Maximum Likelihood (ML) and Restricted Maximum\nLikelihood (REML). It supports flexible modeling of\nbetween-center heterogeneity and is particularly suited for\nmulti-center clinical trials or registries. Core features\ninclude data simulation, likelihood computation,\ncluster-dependent censoring options, and testing of frailty\neffects. For methodological details, see Katsahian et al.\n(2006) <doi:10.1002/sim.2684>.",
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  "URL": "https://github.com/TeamHeKA/FrailtyCompRisk",
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  "Repository": "https://teamheka.r-universe.dev",
  "Date/Publication": "2026-04-23 11:38:45 UTC",
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  "Author": "Benjamin Delmas [aut],\nLucas Ducrot [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8020-2897>),\nSandrine Katsahian [aut] (ORCID:\n<https://orcid.org/0000-0002-7261-0671>),\nInria [cph]",
  "Maintainer": "Lucas Ducrot <lucas.ducrot@inria.fr>",
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    "Ml_CompRisk",
    "Ml_Cox",
    "Parameters_estimation",
    "Reml_CompRisk_frailty",
    "Reml_Cox_frailty",
    "simulate_data"
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    {
      "page": "check_data_format",
      "title": "Check the Format of a Data Frame for Multicentre Competing Risks with Frailty",
      "topics": [
        "check_data_format"
      ]
    },
    {
      "page": "compute_M_optimized",
      "title": "Compute the Weight Matrix M (Optimized)",
      "topics": [
        "compute_M_optimized"
      ]
    },
    {
      "page": "create_G_function",
      "title": "Create Step Function for Estimated Survival (G)",
      "topics": [
        "create_G_function"
      ]
    },
    {
      "page": "KaplanMeier_Censoring_vectorized",
      "title": "Kaplan-Meier Estimator for Censoring Distribution (Vectorized)",
      "topics": [
        "KaplanMeier_Censoring_vectorized"
      ]
    },
    {
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      "title": "Log-Likelihood Contribution for Cause 1",
      "topics": [
        "logLikelihood_1"
      ]
    },
    {
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      "title": "Compute Log-Likelihood Penalty for Random Effects",
      "topics": [
        "logLikelihood_2"
      ]
    },
    {
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      "title": "Maximum Likelihood Estimation for Cause 1 in Competing Risks",
      "topics": [
        "Ml_CompRisk"
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    {
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      "title": "Nelson-Aalen Estimator of Censoring Distribution (Vectorized)",
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        "Nelson_Censoring_vectorized"
      ]
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      "page": "Parameters_estimation",
      "title": "Unified Interface for Parameter Estimation in (Competing Risks) Frailty Models",
      "topics": [
        "Parameters_estimation"
      ]
    },
    {
      "page": "Reml_CompRisk_frailty",
      "title": "REML Estimation for Cause 1 in a Competing Risks Model with Shared Frailty",
      "topics": [
        "Reml_CompRisk_frailty"
      ]
    },
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      "page": "Reml_Cox_frailty",
      "title": "Cox Model with Random Frailty (REML Estimation)",
      "topics": [
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      ]
    },
    {
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      "title": "Simulate clustered competing risks data with shared frailty",
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      ]
    }
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        "Features",
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      "modified": "2025-07-28 13:18:40",
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