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  "Type": "Package",
  "Title": "Univariate Feature Selection and Compound Covariate for\nPredicting Survival, Including Copula-Based Analyses for\nDependent Censoring",
  "Version": "3.33",
  "Date": "2025-6-16",
  "Authors@R": "c(person(given = \"Takeshi\",\nfamily = \"Emura\",\nrole = c(\"aut\", \"cre\"),\nemail = \"takeshiemura@gmail.com\"),\nperson(given = \"Hsuan-Yu\",\nfamily = \"Chen\",\nrole = \"aut\"),\nperson(given = \"Shigeyuki\",\nfamily = \"Matsui\",\nrole = \"aut\"),\nperson(given = \"Yi-Hau\",\nfamily = \"Chen\",\nrole = \"aut\"))",
  "Maintainer": "Takeshi Emura <takeshiemura@gmail.com>",
  "Description": "Univariate feature selection and compound covariate\nmethods under the Cox model with high-dimensional features\n(e.g., gene expressions). Available are survival data for\nnon-small-cell lung cancer patients with gene expressions (Chen\net al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,\nstatistical methods in Emura et al (2012 PLoS ONE)\n<DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat\nMethods Med Res) <DOI:10.1177/0962280214533378>, and Emura et\nal (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for\ngenerating correlated gene expressions are also available.\nEstimation of survival functions via copula-graphic (CG)\nestimators is also implemented, which is useful for sensitivity\nanalyses under dependent censoring (Yeh et al 2023\nBiomedicines) <DOI:10.3390/biomedicines11030797> and factorial\nsurvival analyses (Emura et al 2024 Stat Methods Med Res)\n<DOI:10.1177/09622802231215805>.",
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  "Author": "Takeshi Emura [aut, cre], Hsuan-Yu Chen [aut], Shigeyuki Matsui\n[aut], Yi-Hau Chen [aut]",
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