Fitting Survival Regression Models via 'Stan' R package survstan version 0.0.7.1 CRAN Task View: Survival (https://CRAN.R-project.org/view=Survival) Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>. Software Depends: R (>= 3.4.0), survival Imports: actuar (>= 3.0.0), broom, doFuture, dplyr, extraDistr, foreach, future, generics, ggplot2, gridExtra, MASS, methods, purrr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), Rdpack, rlang, rstan (>= 2.26.0), rstantools (>= 2.3.1), tibble, tidyr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: emmeans (>= 1.4.2), estimability, GGally, knitr, rmarkdown, testthat (>= 3.0.0) Fabio Demarqui <fndemarqui@est.ufmg.br> Comprehensive R Archive Network (CRAN) Fabio Demarqui [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-9236-1986>), Andrew Johnson [ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=survstan/LICENSE) 2024-04-12 application/tgz https://CRAN.R-project.org/package=survstan doi:10.32614/CRAN.package.survstan