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: FrantiÅ¡ek BartoÅ¡ [aut, cre], Julia M. Haaf [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: FrantiÅ¡ek BartoÅ¡ <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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=RoBSA to link to this page.
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