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 medisin.uio.no> |
| BugReports: |
https://github.com/ocbe-uio/BayesSurvive/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/ocbe-uio/BayesSurvive |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
BayesSurvive results |
Documentation:
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