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BSGW  

Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression
View on CRAN: Click here


Download and install BSGW package within the R console
Install from CRAN:
install.packages("BSGW")

Install from Github:
library("remotes")
install_github("cran/BSGW")

Install by package version:
library("remotes")
install_version("BSGW", "0.9.4")



Attach the package and use:
library("BSGW")
Maintained by
Alireza S. Mahani
[Scholar Profile | Author Map]
First Published: 2014-10-12
Latest Update: 2022-12-12
Description:
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.
How to cite:
Alireza S. Mahani (2014). BSGW: Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression. R package version 0.9.4, https://cran.r-project.org/web/packages/BSGW. Accessed 10 Jan. 2025.
Previous versions and publish date:
0.9.1 (2015-09-07 08:45), 0.9.2 (2016-09-21 08:06), 0.9 (2014-10-12 02:02)
Other packages that cited BSGW R package
View BSGW citation profile
Other R packages that BSGW depends, imports, suggests or enhances
Complete documentation for BSGW
Functions, R codes and Examples using the BSGW R package
Some associated functions: bsgw . crossval_bsgw . plot_bsgw . predict_bsgw . summary_bsgw . 
Some associated R codes: BSGW.R . Sample.R . utils.R . zzz.R .  Full BSGW package functions and examples
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