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✨
DESCRIPTION |NEWS
card.svg |card.png
bipd/json (API)

# 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
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

4.32 score 3 stars 23 scripts 327 downloads 12 exports 19 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK183
source / vignettesOK402
linux-release-x86_64OK179
macos-release-arm64OK146
macos-oldrel-arm64OK197
windows-develOK118
windows-releaseOK111
windows-oldrelOK145
wasm-releaseOK156

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

Dependencies:clicodadplyrgenericsgluelatticelifecyclemagrittrmvtnormpillarpkgconfigR6rjagsrlangtibbletidyselectutf8vctrswithr

IPD meta-analysis-with-missing-data
Fitting IPD meta-analysis model with missing data
Rendered fromIPD-meta-analysis-with-missing-data.Rmdusingknitr::rmarkdown

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

IPD meta-analysis
Fitting one-stage IPD meta-analysis model | Incorporating shrinkage and variable selection | Fitting one-stage IPD network meta-analysis
Rendered fromIPD-meta-analysis.Rmdusingknitr::rmarkdown

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

Imputing missing values in IPD
Missing data exploration | Multiple Imputations | Try your data
Rendered fromImputing-missing-values-in-IPD.Rmdusingknitr::rmarkdown

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