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BayesSurvival  

Bayesian Survival Analysis for Right Censored Data
View on CRAN: Click here


Download and install BayesSurvival package within the R console
Install from CRAN:
install.packages("BayesSurvival")

Install from Github:
library("remotes")
install_github("cran/BayesSurvival")

Install by package version:
library("remotes")
install_version("BayesSurvival", "0.2.0")



Attach the package and use:
library("BayesSurvival")
Maintained by
Stephanie van der Pas
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-05-08
Latest Update: 2021-03-12
Description:
Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" . In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.
How to cite:
Stephanie van der Pas (2020). BayesSurvival: Bayesian Survival Analysis for Right Censored Data. R package version 0.2.0, https://cran.r-project.org/web/packages/BayesSurvival. Accessed 13 Aug. 2026.
Previous versions and publish date:
0.1.0 (2020-05-08 20:50), (2026-07-09 07:58)
Other packages that cited BayesSurvival R package
View BayesSurvival citation profile
Other R packages that BayesSurvival depends, imports, suggests or enhances
Complete documentation for BayesSurvival
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