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spBayesSurv  

Bayesian Modeling and Analysis of Spatially Correlated Survival Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("spBayesSurv", "1.1.9")



Attach the package and use:
library("spBayesSurv")
Maintained by
Haiming Zhou
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-06-12
Latest Update: 2025-07-23
Description:
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
How to cite:
Haiming Zhou (2014). spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.9, https://cran.r-project.org/web/packages/spBayesSurv. Accessed 25 Jun. 2026.
Previous versions and publish date:
1.0.0 (2014-06-12 07:40), 1.0.1 (2014-10-25 08:07), 1.0.2 (2014-12-19 07:37), 1.0.3 (2015-09-01 17:35), 1.0.4 (2016-08-17 01:01), 1.0.5 (2016-10-10 18:18), 1.0.6 (2016-12-14 17:23), 1.1.0 (2017-03-31 08:13), 1.1.1 (2017-05-15 07:25), 1.1.2 (2018-01-11 13:36), 1.1.3 (2018-04-23 12:44), 1.1.4 (2020-02-23 15:20), 1.1.5 (2021-01-26 06:30), 1.1.6 (2022-05-15 01:20), 1.1.7 (2023-05-17 21:00), 1.1.8 (2024-02-23 17:30)
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