{
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  "Package": "CopulaCenR",
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  "Title": "Copula-Based Regression Models for Multivariate Censored Data",
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  "Description": "Copula-based regression models for multivariate censored\ndata, including bivariate right-censored data, bivariate\ninterval-censored data, and right/interval-censored\nsemi-competing risks data. Currently supports Clayton, Gumbel,\nFrank, Joe, AMH and Copula2 copula models. For marginal models,\nit supports parametric (Weibull, Loglogistic, Gompertz) and\nsemiparametric (Cox and transformation) models. Includes\nmethods for convenient prediction and plotting. Also provides a\nbivariate time-to-event simulation function and an information\nratio-based goodness-of-fit test for copula. Method details can\nbe found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al\n(2021) Biostatistics, Sun et.al (2022) Statistical Methods in\nMedical Research, Sun et.al (2022) Biometrics, and Sun et al.\n(2023+) JRSSC.",
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  "Author": "Tao Sun [aut, cre] (<https://orcid.org/0000-0003-4447-3005>),\nYing Ding [aut] (<https://orcid.org/0000-0003-1352-1000>)",
  "Maintainer": "Tao Sun <sun.tao@ruc.edu.cn>",
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  "Repository": "https://suntaojj.r-universe.dev",
  "Date/Publication": "2024-11-14 16:50:02 UTC",
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      "page": "AIC.CopulaCenR",
      "title": "the AIC of a CopulaCenR object",
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      "topics": [
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      "page": "tau_copula",
      "title": "Calculate Kendall's tau",
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