Package: shrinkDSM 0.2.0
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:
shrinkDSM_0.2.0.tar.gz
shrinkDSM_0.2.0.tar.gz(r-4.5-noble)shrinkDSM_0.2.0.tar.gz(r-4.4-noble)
shrinkDSM_0.2.0.tgz(r-4.4-emscripten)shrinkDSM_0.2.0.tgz(r-4.3-emscripten)
shrinkDSM.pdf |shrinkDSM.html✨
shrinkDSM/json (API)
| # Install 'shrinkDSM' in R: |
| install.packages('shrinkDSM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- gastric - Survival times of gastric cancer patients
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:73de37c7a9. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 23 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 23 2024 |
Exports:divisionpointsprep_tvinputshrinkDSM
Dependencies:codaGIGrvglatticeRcppRcppArmadilloRcppGSLRcppProgressshrinkTVPstochvolzoo
