Package: BSGW 0.9.4
BSGW: Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
Authors:
BSGW_0.9.4.tar.gz
BSGW_0.9.4.zip(r-4.5)BSGW_0.9.4.zip(r-4.4)BSGW_0.9.4.zip(r-4.3)
BSGW_0.9.4.tgz(r-4.4-any)BSGW_0.9.4.tgz(r-4.3-any)
BSGW_0.9.4.tar.gz(r-4.5-noble)BSGW_0.9.4.tar.gz(r-4.4-noble)
BSGW_0.9.4.tgz(r-4.4-emscripten)BSGW_0.9.4.tgz(r-4.3-emscripten)
BSGW.pdf |BSGW.html✨
BSGW/json (API)
| # Install 'BSGW' in R: |
| install.packages('BSGW', repos = c('https://asmahani.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:d130541a3f. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | OK | Nov 13 2024 |
| R-4.5-linux | OK | Nov 13 2024 |
| R-4.4-win | OK | Nov 13 2024 |
| R-4.4-mac | OK | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
Exports:bsgwbsgw.controlbsgw.crossvalbsgw.crossval.wrapperbsgw.generate.foldsbsgw.generate.folds.eventbalanced
Dependencies:arscodacodetoolsdlmdoParallelforeachiteratorslatticeMatrixMfUSamplersurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian Survival using Generalized Weibull Regression | BSGW bsgw bsgw.control print.bsgw |
| Convenience functions for cross-validation-based selection of shrinkage parameter in the bsgw model. | bsgw.crossval bsgw.crossval.wrapper bsgw.generate.folds bsgw.generate.folds.eventbalanced |
| Plot diagnostics for a bsgw object | plot.bsgw |
| Predict method for bsgw model fits | predict.bsgw summary.predict.bsgw |
| Summarizing Bayesian Survival Generalized Weibull (BSGW) model fits | print.summary.bsgw summary.bsgw |
