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]

bipd_0.3.tar.gz
bipd_0.3.zip(r-4.7)bipd_0.3.zip(r-4.6)bipd_0.3.zip(r-4.5)
bipd_0.3.tgz(r-4.6-any)bipd_0.3.tgz(r-4.5-any)
bipd_0.3.tar.gz(r-4.7-any)bipd_0.3.tar.gz(r-4.6-any)
bipd_0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bipd/json (API)
NEWS

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

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

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

4.26 score 3 stars 20 scripts 351 downloads 12 exports 19 dependencies

Last updated from:35fab266c7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK175
source / vignettesOK467
linux-release-x86_64OK209
macos-release-arm64OK269
macos-oldrel-arm64OK184
windows-develOK138
windows-releaseOK111
windows-oldrelOK112
wasm-releaseOK158

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

Dependencies:clicodadplyrgenericsgluelatticelifecyclemagrittrmvtnormpillarpkgconfigR6rjagsrlangtibbletidyselectutf8vctrswithr

Imputing missing values in IPD

Michael Seo

Rendered fromImputing-missing-values-in-IPD.Rmdusingknitr::rmarkdownon May 21 2026.

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

IPD meta-analysis

Michael Seo

Rendered fromIPD-meta-analysis.Rmdusingknitr::rmarkdownon May 21 2026.

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

IPD meta-analysis-with-missing-data

Michael Seo

Rendered fromIPD-meta-analysis-with-missing-data.Rmdusingknitr::rmarkdownon May 21 2026.

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