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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: |
0.2.0 |
| 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: |
2022-11-15 |
| Author: |
Daniel Winkler [aut, cre],
Peter Knaus  [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:
Downloads:
Linking:
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https://CRAN.R-project.org/package=shrinkDSM
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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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