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  "Title": "Random Generation of Survival Data",
  "Version": "0.0.3",
  "Date": "2026-01-23",
  "Author": "Fabio Demarqui [aut, cre, cph]\n(<https://orcid.org/0000-0001-9236-1986>)",
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  "Description": "Random generation of survival data from a wide range of\nregression models, including accelerated failure time (AFT),\nproportional hazards (PH), proportional odds (PO), accelerated\nhazard (AH), Yang and Prentice (YP), and extended hazard (EH)\nmodels. The package 'rsurv' also stands out by its ability to\ngenerate survival data from an unlimited number of baseline\ndistributions provided that an implementation of the quantile\nfunction of the chosen baseline distribution is available in R.\nAnother nice feature of the package 'rsurv' lies in the fact\nthat linear predictors are specified via a formula-based\napproach, facilitating the inclusion of categorical variables\nand interaction terms. The functions implemented in the package\n'rsurv' can also be employed to simulate survival data with\nmore complex structures, such as survival data with different\ntypes of censoring mechanisms, left-, right-, and\ndouble-truncated survival data, survival data with cure\nfraction, survival data with random effects (frailties),\nmultivariate survival data, and competing risks survival data.\nDetails about the R package 'rsurv' can be found in Demarqui\n(2024) <doi:10.48550/arXiv.2406.01750>.",
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