This package is considered a duplicate. The official version of this package is found at:https://dwinkler1.r-universe.dev/shrinkDSM

Package: shrinkDSM 1.0.2

Daniel Winkler

shrinkDSM: Efficient Bayesian Inference for Dynamic Survival Models with Shrinkage

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) <doi:10.1007/s11222-009-9164-5>, Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.

Authors:Daniel Winkler [aut, cre], Peter Knaus [aut]

# Install 'shrinkDSM' in R:

install.packages('shrinkDSM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

Last updated from:94874682ad. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	152
linux-devel-x86_64	OK	159
source / vignettes	OK	204
linux-release-arm64	OK	165
linux-release-x86_64	OK	150
wasm-release	OK	128

Help page	Topics
Create division points for estimation of a dynamic survival model	divisionpoints
Survival times of gastric cancer patients	gastric
Graphical summary of posterior distribution for a piecewise constant, time-varying parameter	plot.mcmc.dsm.tvp
Graphical summary of posterior distribution of fitted dynamic survival model	plot.shrinkDSM
Graphical summary of posterior predictive density	plot.shrinkDSM_pred
Draw from posterior predictive density of a fitted time-varying parameter survival model	predict.shrinkDSM
Prepare time-varying inputs for estimation of a dynamic survival model	prep_tvinput
Nicer printing of shrinkDSM objects	print.shrinkDSM
Markov Chain Monte Carlo (MCMC) for time-varying parameter survival models with shrinkage	shrinkDSM