Package: PRP 0.1.1
PRP: Bayesian Prior and Posterior Predictive Replication Assessment
Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) <arxiv:2105.03993>.
Authors:
PRP_0.1.1.tar.gz
PRP_0.1.1.zip(r-4.5)PRP_0.1.1.zip(r-4.4)PRP_0.1.1.zip(r-4.3)
PRP_0.1.1.tgz(r-4.4-any)PRP_0.1.1.tgz(r-4.3-any)
PRP_0.1.1.tar.gz(r-4.5-noble)PRP_0.1.1.tar.gz(r-4.4-noble)
PRP_0.1.1.tgz(r-4.4-emscripten)PRP_0.1.1.tgz(r-4.3-emscripten)
PRP.pdf |PRP.html✨
PRP/json (API)
| # Install 'PRP' in R: |
| install.packages('PRP', repos = c('https://artemiszhao.r-universe.dev', 'https://cloud.r-project.org')) |
- RPP_filtered - Filtered RPP data
- mortality - Cardiovascular disease impact on the mortality of COVID-19
- severity - Cardiovascular disease impact on the severe case rate of COVID-19
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:eec111a4a6. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 17 2024 |
| R-4.5-win | OK | Sep 17 2024 |
| R-4.5-linux | OK | Sep 17 2024 |
| R-4.4-win | OK | Sep 17 2024 |
| R-4.4-mac | OK | Sep 17 2024 |
| R-4.3-win | OK | Sep 17 2024 |
| R-4.3-mac | OK | Sep 17 2024 |
Exports:eggerposterior_prpprior_prpQ
Dependencies:mvtnorm
