survstan: Fitting Survival Regression Models via 'Stan'

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, and Yang and Prentice models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, 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>.

Version: 0.0.4
Depends: R (≥ 3.4.0), survival
Imports: actuar (≥ 3.0.0), dplyr, extraDistr, 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 (≥, RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
Suggests: GGally, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-09-21
Author: Fabio Demarqui ORCID iD [aut, cre, cph]
Maintainer: Fabio Demarqui <fndemarqui at>
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: survstan results


Reference manual: survstan.pdf
Vignettes: Likelihood ratio tests with the survstan package
Introduction to the R package survstan


Package source: survstan_0.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): survstan_0.0.4.tgz, r-oldrel (arm64): survstan_0.0.4.tgz, r-release (x86_64): survstan_0.0.4.tgz, r-oldrel (x86_64): survstan_0.0.4.tgz
Old sources: survstan archive


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