Package: bipd 0.3

bipd: Bayesian Individual Patient Data Meta-Analysis using 'JAGS'

We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <doi:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

Authors:Michael Seo [aut, cre]

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bipd.pdf |bipd.html
bipd/json (API)
NEWS

# Install 'bipd' in R:
install.packages('bipd', repos = c('https://mikejseo.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mikejseo/bipd/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

4.26 score 3 stars 20 scripts 277 downloads 12 exports 20 dependencies

Last updated 2 years agofrom:35fab266c7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:add.mcmcfindMissingPatterngenerate_ipdma_examplegenerate_ipdnma_examplegenerate_sysmiss_ipdma_exampleipd.runipd.run.parallelipdma.imputeipdma.model.deft.onestageipdma.model.onestageipdnma.model.onestagetreatment.effect

Dependencies:clicodadplyrfansigenericsgluelatticelifecyclemagrittrmvtnormpillarpkgconfigR6rjagsrlangtibbletidyselectutf8vctrswithr

Imputing missing values in IPD

Rendered fromImputing-missing-values-in-IPD.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2022-01-22
Started: 2022-01-21

IPD meta-analysis

Rendered fromIPD-meta-analysis.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2022-05-25
Started: 2022-01-21

IPD meta-analysis-with-missing-data

Rendered fromIPD-meta-analysis-with-missing-data.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2022-05-25
Started: 2022-03-03