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]

shrinkDSM_1.0.2.tar.gz
shrinkDSM_1.0.2.zip(r-4.7-x86_64)shrinkDSM_1.0.2.zip(r-4.7-arm64)shrinkDSM_1.0.2.zip(r-4.6-x86_64)shrinkDSM_1.0.2.zip(r-4.6-arm64)
shrinkDSM_1.0.2.tar.gz(r-4.7-arm64)shrinkDSM_1.0.2.tar.gz(r-4.7-x86_64)shrinkDSM_1.0.2.tar.gz(r-4.6-arm64)shrinkDSM_1.0.2.tar.gz(r-4.6-x86_64)
shrinkDSM_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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 395 downloads 4 exports 12 dependencies

Last updated from:94874682ad. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK153
linux-devel-x86_64OK156
source / vignettesOK180
linux-release-arm64OK155
linux-release-x86_64OK172
windows-devel-arm64OK220
windows-devel-x86_64OK201
windows-release-arm64OK271
windows-release-x86_64OK145
wasm-releaseOK141

Exports:divisionpointsprep_tvinputshrinkDSMSurv

Dependencies:codaGIGrvglatticeMatrixRcppRcppArmadilloRcppGSLRcppProgressshrinkTVPstochvolsurvivalzoo