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>.

Version: 0.1.0
Depends: R (≥ 3.3.0)
Imports: Rcpp, stochvol (≥ 3.0.3), coda, utils, shrinkTVP (≥ 2.0.2)
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, stochvol, shrinkTVP
Suggests: testthat (≥ 3.0.0)
Published: 2021-09-06
Author: Daniel Winkler [aut, cre], Peter Knaus ORCID iD [aut]
Maintainer: Daniel Winkler <daniel.winkler at wu.ac.at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: shrinkDSM results

Documentation:

Reference manual: shrinkDSM.pdf

Downloads:

Package source: shrinkDSM_0.1.0.tar.gz
Windows binaries: r-devel: shrinkDSM_0.1.0.zip, r-devel-UCRT: shrinkDSM_0.1.0.zip, r-release: shrinkDSM_0.1.0.zip, r-oldrel: shrinkDSM_0.1.0.zip
macOS binaries: r-release (arm64): shrinkDSM_0.1.0.tgz, r-release (x86_64): shrinkDSM_0.1.0.tgz, r-oldrel: shrinkDSM_0.1.0.tgz

Linking:

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