This package is considered a duplicate. The official version of this package is found at:https://ocbe-uio.r-universe.dev/BayesSurvive

Package: BayesSurvive 0.1.0

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.tar.gz
BayesSurvive_0.1.0.tar.gz(r-4.7-arm64)BayesSurvive_0.1.0.tar.gz(r-4.6-arm64)BayesSurvive_0.1.0.tar.gz(r-4.7-x86_64)BayesSurvive_0.1.0.tar.gz(r-4.6-x86_64)
BayesSurvive_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BayesSurvive/json (API)
NEWS

# Install 'BayesSurvive' in R:

install.packages('BayesSurvive', repos = c('https://cran.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:

simData - Simulated survival data

On CRAN:

openblas cpp openmp

2.70 score 1 scripts 259 downloads 7 exports 127 dependencies

Last updated from:e29879a94c. Checks:2 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	NOTE	305
source / vignettes	OK	334
linux-release-x86_64	NOTE	314
wasm-release	OK	183

Exports:BayesSurvive func_MCMC func_MCMC_graph plotBrier UpdateGamma UpdateRPlee11 VS

Dependencies:backports base64enc brio bslib cachem callr checkmate cli cluster cmprsk codetools colorspace cpp11 crayon data.table desc diagram diffobj digest doParallel dplyr evaluate farver fastmap fontawesome forcats foreach foreign Formula fs future future.apply generics GGally ggplot2 ggstats glmnet globals glue gridExtra gtable highr Hmisc hms htmlTable htmltools htmlwidgets isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv magrittr MASS Matrix MatrixModels memoise mets mime multcomp mvtnorm nlme nnet numDeriv parallelly patchwork pillar pkgbuild pkgconfig pkgload plotrix polspline praise prettyunits processx prodlim progress progressr ps Publish purrr quantreg R6 ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen riskRegression rlang rmarkdown rms rpart rprojroot rstudioapi S7 sandwich sass scales shape SparseM SQUAREM stringi stringr survival testthat TH.data tibble tidyr tidyselect timereg tinytex utf8 vctrs viridisLite waldo withr xfun yaml zoo

Bayesian Cox models with graph-structured variable selection priors

Rendered fromBayesCox.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2025-03-25
Started: 2024-06-05

Help page	Topics
Fit Bayesian Cox Models	BayesSurvive
Create a dataframe of estimated coefficients	coef.BayesSurvive
Function to run MCMC sampling	func_MCMC
Function to learn MRF graph	func_MCMC_graph
Create a plot of estimated coefficients	plot.BayesSurvive
Time-dependent Brier scores	plotBrier
Predict survival risk	predict.BayesSurvive
Simulated survival data	simData
Subfunctions to update parameters	UpdateGamma
Update coefficients of Bayesian Cox Models	UpdateRPlee11
Function to perform variable selection	VS

Package: BayesSurvive 0.1.0

BayesSurvive: Bayesian Survival Models for High-Dimensional Data

Bayesian Cox models with graph-structured variable selection priors

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)