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-4.7-any)PRP_0.1.1.zip(r-4.6-any)PRP_0.1.1.zip(r-4.5-any)
PRP_0.1.1.tgz(r-4.6-any)PRP_0.1.1.tgz(r-4.5-any)
PRP_0.1.1.tar.gz(r-4.7-any)PRP_0.1.1.tar.gz(r-4.6-any)
PRP_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PRP/json (API)

# Install 'PRP' in R:
install.packages('PRP', repos = c('https://artemiszhao.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • mortality - Cardiovascular disease impact on the mortality of COVID-19
  • RPP_filtered - Filtered RPP data
  • severity - Cardiovascular disease impact on the severe case rate of COVID-19

On CRAN:

Conda:

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 285 downloads 4 mentions 4 exports 1 dependencies

Last updated from:eec111a4a6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK87
source / vignettesOK130
linux-releaseOK84
macos-releaseOK159
macos-oldrelOK216
windows-develOK59
windows-releaseOK67
windows-oldrelOK54
wasm-releaseOK72

Exports:eggerposterior_prpprior_prpQ

Dependencies:mvtnorm