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shrinkDSM (version 1.0.0)

Efficient Bayesian Inference for Dynamic Survival Models with Shrinkage

Description

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of dynamic survival models with shrinkage priors. Details on the algorithms used are provided in Wagner (2011) , Bitto and Frühwirth-Schnatter (2019) and Cadonna et al. (2020) .

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Version

Install

install.packages('shrinkDSM')

Monthly Downloads

204

Version

1.0.0

License

GPL (>= 2)

Maintainer

Daniel Winkler

Last Published

April 11th, 2025

Functions in shrinkDSM (1.0.0)

plot.shrinkDSM_pred

Graphical summary of posterior predictive density
gastric

Survival times of gastric cancer patients
print.shrinkDSM

Nicer printing of shrinkDSM objects
prep_tvinput

Prepare time-varying inputs for estimation of a dynamic survival model
divisionpoints

Create division points for estimation of a dynamic survival model
predict.shrinkDSM

Draw from posterior predictive density of a fitted time-varying parameter survival model
plot.mcmc.dsm.tvp

Graphical summary of posterior distribution for a piecewise constant, time-varying parameter
shrinkDSM

Markov Chain Monte Carlo (MCMC) for time-varying parameter survival models with shrinkage
plot.shrinkDSM

Graphical summary of posterior distribution of fitted dynamic survival model
reexports

Objects exported from other packages