doi:10.1186/s12874-022-01676-9>). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration.">

RoBSA: Robust Bayesian Survival Analysis (original) (raw)

A framework for estimating ensembles of parametric survival models with different parametric families. The RoBSA framework uses Bayesian model-averaging to combine the competing parametric survival models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual predictors or preference for a parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration.

Version: 1.0.3
Depends: R (≥ 4.0.0)
Imports: BayesTools (≥ 0.2.14), survival, rjags, runjags, scales, coda, stats, graphics, rlang, Rdpack
Suggests: parallel, ggplot2, flexsurv, testthat, vdiffr, knitr, rmarkdown, covr
Published: 2025-03-24
DOI: 10.32614/CRAN.package.RoBSA
Author: František Bartoš ORCID iD [aut, cre], Julia M. Haaf ORCID iD [ths], Matthew Denwood [cph] (Original copyright holder of some modified code where indicated.), Martyn Plummer [cph] (Original copyright holder of some modified code where indicated.)
Maintainer: František Bartoš <f.bartos96 at gmail.com>
BugReports: https://github.com/FBartos/RoBSA/issues
License: GPL-3
URL: https://fbartos.github.io/RoBSA/
NeedsCompilation: yes
SystemRequirements: JAGS >= 4.3.1 (https://mcmc-jags.sourceforge.io/)
Citation: RoBSA citation info
Materials: README, NEWS
CRAN checks: RoBSA results

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