shrinkDSM
1.0.2Efficient Bayesian Inference for Dynamic Survival Models with Shrinkage
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3.9Kdownloads / year
74.0%test coverage
13/13checks pass
Overview
About
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.
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13OK
Slowest check: 3.6 min · r-oldrel-windows-x86_64
Code health
Yes
Tests · ratio 0.06
74.0%
Coverage · measured lines
100%
Documentation · exports
6
Dependencies · direct
Check history
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-04-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Documentation
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- Examples that run
- 38%
- Documented parameters
- 99%
- Return-value docs
- 75%
- References docs
- 0%
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Also on413 r2u17 autocran
Dependencies
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10 external dependencies (excludes base and recommended)
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R >= 3.3.0
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Package metadata
- First published
- 2021-09-06
- Total releases
- 4 / 5 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.3.0
- Bundled data
- 0.6 KB / 1 file
- Download size
- 94 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet