{
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  "Type": "Package",
  "Title": "Tractable Parametric Odds-Based Regression Models",
  "Authors@R": "c(person(given = \"Abdisalam\", family = \"Hassan Muse\",\nrole = c(\"aut\", \"cre\"), email =\n\"abdisalam.h.muse@gmail.com\", comment = c(ORCID = \"0000-0003-4905-0044\")),person(given = \"Samuel\",\nfamily = \"Mwalili\", role = c(\"aut\",\"ctb\"), email =\n\"samuel.mwalili@gmail.com\"), person(given = \"Oscar\",\nfamily = \"Ngesa\", role = c(\"aut\",\"ctb\"), email =\n\"oscanges@gmail.com\"),person(given = \"Christophe\",\nfamily = \"Chesneau\", role = c(\"aut\",\"ctb\"), email =\n\"christophe.chesneau@gmail.com\"))",
  "Version": "0.1.0",
  "Maintainer": "Abdisalam Hassan Muse <abdisalam.h.muse@gmail.com>",
  "Description": "Fits tractable fully parametric odds-based regression\nmodels for survival data, including proportional odds (PO),\naccelerated failure time (AFT), accelerated odds (AO), and\nGeneral Odds (GO) models in overall survival frameworks. Given\nat least an R function specifying the survivor, hazard rate and\ncumulative distribution functions, any user-defined parametric\ndistribution can be fitted. We applied and evaluated a minimum\nof seventeen (17) various baseline distributions that can\nhandle different failure rate shapes for each of the four\ndifferent proposed odds-based regression models. For more\ninformation see Bennet et al., (1983)\n<doi:10.1002/sim.4780020223>, and Muse et al., (2022)\n<doi:10.1016/j.aej.2022.01.033>.",
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  "Packaged": {
    "Date": "2026-05-16 06:46:54 UTC",
    "User": "root"
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  "Author": "Abdisalam Hassan Muse [aut, cre]\n(<https://orcid.org/0000-0003-4905-0044>), Samuel Mwalili [aut,\nctb], Oscar Ngesa [aut, ctb], Christophe Chesneau [aut, ctb]",
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  "Repository": "https://abdisalammuse.r-universe.dev",
  "Date/Publication": "2022-09-08 08:12:56 UTC",
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  "_published": "2026-06-02T18:40:46.932Z",
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      ]
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