BayesSurvive: Bayesian Survival Models for High-Dimensional Data

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database.

Version: 0.0.2
Depends: R (≥ 4.0)
Imports: Rcpp, ggplot2, GGally, mvtnorm, survival, riskRegression, utils, stats
LinkingTo: Rcpp, RcppArmadillo (≥ 0.9.000)
Suggests: knitr
Published: 2024-06-04
DOI: 10.32614/CRAN.package.BayesSurvive
Author: Zhi Zhao [aut, cre], Katrin Madjar [aut], Tobias Østmo Hermansen [aut], Manuela Zucknick [ctb], Jörg Rahnenführer [ctb]
Maintainer: Zhi Zhao <zhi.zhao at>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: BayesSurvive results


Reference manual: BayesSurvive.pdf
Vignettes: Bayesian Cox Models with graph-structure priors


Package source: BayesSurvive_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BayesSurvive_0.0.2.tgz, r-oldrel (arm64): BayesSurvive_0.0.2.tgz, r-release (x86_64): BayesSurvive_0.0.2.tgz, r-oldrel (x86_64): BayesSurvive_0.0.2.tgz
Old sources: BayesSurvive archive


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