| Title: | Bayesian Estimation for Alpha-Mixture Survival Models |
|---|---|
| Description: | Implements Bayesian estimation and inference for alpha-mixture survival models, including Weibull and Exponential based components, with tools for simulation and posterior summaries. The methods target applications in reliability and biomedical survival analysis. The package implements Bayesian estimation for the alpha-mixture methodology introduced in Asadi et al. (2019) <doi:10.1017/jpr.2019.72>. |
| Authors: | Feng Luan [aut, cre], Zhexuan Yang [aut], Duchwan Ryu [aut] |
| Maintainer: | Feng Luan <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2026-05-20 07:56:07 UTC |
| Source: | https://github.com/cran/alpmixBayes |
Main Bayesian Mixture Model Function
alpmixBayes( d, mcmc_values = NULL, init_values = NULL, prior = NULL, survmodel = c("WW", "EW", "LL", "EWG"), verbose = TRUE, ... )alpmixBayes( d, mcmc_values = NULL, init_values = NULL, prior = NULL, survmodel = c("WW", "EW", "LL", "EWG"), verbose = TRUE, ... )
d |
Input data |
mcmc_values |
MCMC parameters |
init_values |
Initial values |
prior |
Prior distributions |
survmodel |
Survival model type |
verbose |
Logical. If TRUE, progress messages are printed. Default is TRUE. |
... |
Additional arguments |
Bayesian mixture model results
This runs alpmixBayes on the packaged example dataset ewm1.100.
demo_run(verbose = TRUE)demo_run(verbose = TRUE)
verbose |
Logical. If TRUE, progress messages are printed. Default is TRUE. |
Summary of the Bayesian mixture model
# Run the demo (may take a few moments) demo_run()# Run the demo (may take a few moments) demo_run()
Demonstration dataset for Exponential-Weibull mixture models. Contains 5 simulated datasets for examples and testing.
ewew
A list with 5 components, each containing mixture model data
Simulated data
Demonstration dataset for Exponential-Weibull-Gamma mixture models. Contains 5 simulated datasets for examples and testing.
ewgewg
A list with 5 components, each containing:
Simulated data
data(ewg) str(ewg, max.level = 1) # Extract data from first element y_data <- ewg[[1]]$ydata(ewg) str(ewg, max.level = 1) # Extract data from first element y_data <- ewg[[1]]$y
Exponential-Weibull-Gamma Mixture Model
ewgmix(d, init_values, mcmc_values, prior)ewgmix(d, init_values, mcmc_values, prior)
d |
Input data |
init_values |
Initial values |
mcmc_values |
MCMC parameters |
prior |
Prior distributions |
Model results
Exponential-Weibull Mixture Model
ewmix(d, init_values, mcmc_values, prior)ewmix(d, init_values, mcmc_values, prior)
d |
Input data |
init_values |
Initial values |
mcmc_values |
MCMC parameters |
prior |
Prior distributions |
Model results
Demonstration dataset for Log-Logistic mixture models. Contains 5 simulated datasets for examples and testing.
llll
A list with 5 components, each containing mixture model data
Simulated data
Lognormal-Lognormal Mixture Model
llmix(d, init_values, mcmc_values, prior)llmix(d, init_values, mcmc_values, prior)
d |
Input data |
init_values |
Initial values |
mcmc_values |
MCMC parameters |
prior |
Prior distributions |
Model results
Summary method for alpmixBayes objects
## S3 method for class 'alpmixBayes' summary(object, ...)## S3 method for class 'alpmixBayes' summary(object, ...)
object |
An alpmixBayes object |
... |
Additional arguments passed to summary |
A data frame with parameter estimates and credible intervals
Demonstration dataset for Weibull mixture models. Contains 5 simulated datasets for examples and testing.
wwww
A list with 5 components, each containing mixture model data
Simulated data
Weibull-Weibull Mixture Model
wwmix(d, init_values, mcmc_values, prior)wwmix(d, init_values, mcmc_values, prior)
d |
Input data |
init_values |
Initial values |
mcmc_values |
MCMC parameters |
prior |
Prior distributions |
Model results