{
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  "Package": "survstan",
  "Title": "Fitting Survival Regression Models via 'Stan'",
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  "Authors@R": "c(\nperson(given = \"Fabio\", family = \"Demarqui\", role = c(\"aut\", \"cre\", \"cph\"), email = \"fndemarqui@est.ufmg.br\", comment = c(ORCID = \"0000-0001-9236-1986\")),\nperson(given = \"Andrew\", family = \"Johnson\", role = \"ctb\")\n)",
  "Description": "Parametric survival regression models under the maximum\nlikelihood approach via 'Stan'. Implemented regression models\ninclude accelerated failure time (AFT) models, proportional\nhazards (PH) models, proportional odds (PO) models, accelerated\nhazard (AH) models, Yang and Prentice (YP) models, and extended\nhazard (EH) models. Available baseline survival distributions\ninclude exponential, Weibull, log-normal, log-logistic, gamma,\ngeneralized gamma, rayleigh, Gompertz and fatigue\n(Birnbaum-Saunders) distributions. The baseline survival\ndistribution can be further modeled using Bernstein\npolynomails' approximation of the baseline hazard function.\nReferences: Lawless (2002) <ISBN:9780471372158>; Bennett (1982)\n<doi:10.1002/sim.4780020223>; Chen and Wang(2000)\n<doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink\n(2021) <doi:10.1214/20-BJPS471>.",
  "License": "MIT + file LICENSE",
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  "URL": "https://github.com/fndemarqui/survstan,\nhttps://fndemarqui.github.io/survstan/",
  "BugReports": "https://github.com/fndemarqui/survstan/issues",
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  "Repository": "https://fndemarqui.r-universe.dev",
  "Date/Publication": "2025-11-17 17:19:31 UTC",
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  "Author": "Fabio Demarqui [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-9236-1986>),\nAndrew Johnson [ctb]",
  "Maintainer": "Fabio Demarqui <fndemarqui@est.ufmg.br>",
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      "title": "The 'survstan' package.",
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        "survstan"
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      "title": "Fitting Accelerated Failure Time Models",
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      "title": "Fitting Accelerated Hazard Models",
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      "title": "anova method for survstan models",
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      "title": "Bernstein polynomial",
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      "title": "Generic S3 method cross_time",
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      "title": "residuals method for survstan models",
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