R package spBayesSurv. Provides several Bayesian survival models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized accelerated failure time frailty models, and standard semiparametric frailty models within the context of proportional hazards, proportional odds and accelerated failure time.
Keywords for this software
References in zbMATH (referenced in 7 articles , 2 standard articles )
Showing results 1 to 7 of 7.
- Minnie M. Joo, Nicolás Schmidt, Sergio Béjar, Vineeta Yadav, Bumba Mukherjee: BayesMFSurv: An R Package to Estimate Bayesian Split-Population Survival Models With (and Without) Misclassified Failure Events (2020) not zbMATH
- Zhang, Jiajia; Hanson, Timothy; Zhou, Haiming: Bayes factors for choosing among six common survival models (2019)
- Zhou, Haiming; Hanson, Timothy: A unified framework for Fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data (2018)
- Benjamin Taylor and Barry Rowlingson: spatsurv: An R Package for Bayesian Inference with Spatial Survival Models (2017) not zbMATH
- Haiming Zhou, Timothy Hanson, Jiajia Zhang: spBayesSurv: Fitting Bayesian Spatial Survival Models Using R (2017) arXiv
- Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia: Generalized accelerated failure time spatial frailty model for arbitrarily censored data (2017)
- Zhou, Haiming; Hanson, Timothy; Knapp, Roland: Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations (2015)