Package: BayesSurvive 0.1.0.9004

Zhi Zhao

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 (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

Authors:Zhi Zhao [aut, cre], Waldir Leoncio [aut], Katrin Madjar [aut], Tobias Østmo Hermansen [aut], Manuela Zucknick [ctb], Jörg Rahnenführer [ctb]

BayesSurvive_0.1.0.9004.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
BayesSurvive/json (API)
NEWS

# Install 'BayesSurvive' in R:
install.packages('BayesSurvive', repos = c('https://ocbe-uio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ocbe-uio/bayessurvive/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

bayesian-cox-modelsbayesian-variable-selectiongraph-learninghigh-dimensional-statisticsomics-data-integrationsurvival-analysisopenblascppopenmp

4.78 score 4 stars 1 scripts 269 downloads 7 exports 127 dependencies

Last updated from:3c82e993e3. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK275
linux-devel-x86_64OK313
source / vignettesOK305
linux-release-arm64OK269
linux-release-x86_64OK311
macos-release-arm64OK301
macos-release-x86_64OK469
macos-oldrel-arm64OK192
macos-oldrel-x86_64OK477
windows-develOK367
windows-releaseOK289
windows-oldrelOK346
wasm-releaseOK174

Exports:BayesSurvivefunc_MCMCfunc_MCMC_graphplotBrierUpdateGammaUpdateRPlee11VS

Dependencies:backportsbase64encbriobslibcachemcallrcheckmatecliclustercmprskcodetoolscolorspacecpp11crayondata.tabledescdiagramdiffobjdigestdoParalleldplyrevaluatefarverfastmapfontawesomeforcatsforeachforeignFormulafsfuturefuture.applygenericsGGallyggplot2ggstatsglmnetglobalsgluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvmagrittrMASSMatrixMatrixModelsmemoisemetsmimemultcompmvtnormnlmennetnumDerivparallellypatchworkpillarpkgbuildpkgconfigpkgloadplotrixpolsplinepraiseprettyunitsprocessxprodlimprogressprogressrpsPublishpurrrquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenriskRegressionrlangrmarkdownrmsrpartrprojrootrstudioapiS7sandwichsassscalesshapeSparseMSQUAREMstringistringrsurvivaltestthatTH.datatibbletidyrtidyselecttimeregtinytexutf8vctrsviridisLitewaldowithrxfunyamlzoo

Bayesian Cox models with graph-structured variable selection priors

Rendered fromBayesCox.Rmdusingknitr::rmarkdownon May 17 2026.

Last update: 2025-03-20
Started: 2024-04-23