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

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'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • gastric - Survival times of gastric cancer patients

On CRAN:

Conda:

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

openblascpp

1.48 score 3 stars 2 scripts 222 downloads 3 exports 10 dependencies

Last updated 2 years agofrom:73de37c7a9. Checks:1 OK, 1 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 20 2025
R-4.5-linux-x86_64NOTEFeb 20 2025

Exports:divisionpointsprep_tvinputshrinkDSM

Dependencies:codaGIGrvglatticeRcppRcppArmadilloRcppGSLRcppProgressshrinkTVPstochvolzoo