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survregVB is an R package that provides Bayesian
inference for log-logistic accelerated failure time (AFT) models used in
survival analysis as a faster alternative to Markov chain Monte Carlo
(MCMC) methods. The details of the Variational Bayes algorithms with and
without shared frailty can be found in Xian et al.,
(2024a) and Xian
et al., (2024b) respectively.
To install survregVB, use the following command:
remotes::install_github("https://github.com/chengqianxian/survregVB")library(survregVB)
library(survival) # Example using dataset included in the package
data(dnase)
# Fit a survival model
fit <- survregVB(formula = Surv(time, infect) ~ trt + fev, data = dnase,
alpha_0 = 501, omega_0 = 500, mu_0 = c(4.4, 0.25, 0.04), v_0 = 1)
# Print summary
summary(fit)# Using dataset included in the package
data(simulation_frailty)
# Fit a survival model with shared frailty
fit_frailty <- survregVB(formula = Surv(Time.15, delta.15) ~ x1 + x2, data = simulation_frailty,
alpha_0 = 3, omega_0 = 2, mu_0 = c(0, 0, 0), v_0 = 0.1,
lambda_0 = 3, eta_0 = 2, cluster = cluster)
# Print summary
summary(fit_frailty)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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