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:Yi Zhao [aut, cre], Xiaoquan Wen [aut]

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'))

Peer review:

Datasets:
  • 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

On CRAN:

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

2.30 score 3 scripts 280 downloads 4 mentions 4 exports 1 dependencies

Last updated 3 years agofrom:eec111a4a6. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-winOKNov 16 2024
R-4.5-linuxOKNov 16 2024
R-4.4-winOKNov 16 2024
R-4.4-macOKNov 16 2024
R-4.3-winOKNov 16 2024
R-4.3-macOKNov 16 2024

Exports:eggerposterior_prpprior_prpQ

Dependencies:mvtnorm