Package: bayesmeta 3.5

bayesmeta: Bayesian Random-Effects Meta-Analysis and Meta-Regression

A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022) <doi:10.1016/j.cmpb.2022.107303>.

Authors:Christian Roever [aut, cre], Tim Friede [ctb]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bayesmeta/json (API)

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

On CRAN:

Conda:

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

6.15 score 3 stars 2 packages 120 scripts 2.4k downloads 9 mentions 68 exports 14 dependencies

Last updated from:3f62ec46f8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK150
source / vignettesOK183
linux-releaseOK137
macos-releaseOK189
macos-oldrelOK237
windows-develOK122
windows-releaseOK113
windows-oldrelOK114
wasm-releaseOK87

Exports:bayesmetabmrconvolvedhalfcauchydhalflogisticdhalfnormaldhalftdinvchidlomaxdrayleighehalfcauchyehalflogisticehalfnormalehalfteinvchielomaxerayleighessforestplot.bayesmetaforestplot.bmrforestplot.escalcfunnel.bayesmetakldivnormalmixturepairs.bmrphalfcauchyphalflogisticphalfnormalphalftpinvchiplomaxplot.bayesmetaplot.bmrpppvalueprayleighprint.bayesmetaprint.bmrprint.summary.bmrqhalfcauchyqhalflogisticqhalfnormalqhalftqinvchiqlomaxqrayleighrhalfcauchyrhalflogisticrhalfnormalrhalftRhodesEtAlParametersRhodesEtAlPriorrinvchirlomaxrrayleighsummary.bayesmetasummary.bmrtraceplotTurnerEtAlParametersTurnerEtAlPrioruisdvhalfcauchyvhalflogisticvhalfnormalvhalftvinvchivlomaxvrayleighweightsplot

Dependencies:abindbackportscheckmatedigestforestplotlatticemathjaxrMatrixmetadatmetaformvtnormnlmenumDerivpbapply

An introduction to meta-analysis using the bayesmeta package
The problem | Estimates and standard errors | Computing effect sizes | The random effects model | Parameter estimation in the random effects model | The technical side | Prior & posterior | Prior information for the effect parameter $\mu$ | Prior information for the heterogeneity parameter $\tau$ | Inference | The practical side | A simple example | More detailed analysis | References
Rendered frombayesmeta.Rmdusingknitr::rmarkdown

Last update: 2025-08-29
Started: 2015-12-16

Using the bayesmeta R package for Bayesian random-effects meta-regression
Rendered fromRoeverFriede2023-UsingBayesmetaForMetaRegression.pdf.asisusingR.rsp::asis

Last update: 2023-06-27
Started: 2023-06-27

Bayesian random-effects meta-analysis using the bayesmeta R package
Introduction | Random-effects meta-analysis | Using the bayesmeta package | Summary | Unit information priors for binary outcomes | Conservatism of the uniform heterogeneity prior | Marginal likelihood derivation | Mixture implementation details | Calibration check
Rendered fromRoever2020-bayesmeta.pdf.asisusingR.rsp::asis

Last update: 2020-12-15
Started: 2020-12-15

Readme and manuals

Help Manual

Help pageTopics
Bayesian Random-Effects Meta-Analysis and Meta-Regressionbayesmeta-package
Ankylosing spondylitis example dataBaetenEtAl2013
Bayesian random-effects meta-analysisbayesmeta bayesmeta.default bayesmeta.escalc print.bayesmeta summary.bayesmeta
Bayesian random-effects meta-regressionbmr bmr.default bmr.escalc pairs.bmr plot.bmr print.bmr
Direct and indirect comparison example dataBucherEtAl1997
Fly counts example dataCochran1954
Convolution of two probability distributionsconvolve
Pediatric liver transplant example dataCrinsEtAl2014
Half-logistic distribution.dhalflogistic ehalflogistic phalflogistic qhalflogistic rhalflogistic vhalflogistic
Half-normal, half-Student-t and half-Cauchy distributions.dhalfcauchy dhalfnormal dhalft ehalfcauchy ehalfnormal ehalft phalfcauchy phalfnormal phalft qhalfcauchy qhalfnormal qhalft rhalfcauchy rhalfnormal rhalft vhalfcauchy vhalfnormal vhalft
Inverse-Chi distribution.dinvchi einvchi pinvchi qinvchi rinvchi vinvchi
The Lomax distribution.dlomax elomax plomax qlomax rlomax vlomax
The Rayleigh distribution.drayleigh erayleigh prayleigh qrayleigh rrayleigh vrayleigh
Effective sample size (ESS)ess ess.bayesmeta
Generate a forest plot for a 'bayesmeta' object (based on the 'metafor' package's plotting functions).forest.bayesmeta
Generate a forest plot for a 'bayesmeta' object (based on the 'forestplot' package's plotting functions).forestplot.bayesmeta
Generate a forest plot for a 'bmr' object (based on the 'forestplot' package's plotting functions).forestplot.bmr
Generate a forest plot for an 'escalc' object (based on the 'forestplot' package's plotting functions).forestplot.escalc
Generate a funnel plot for a 'bayesmeta' object.funnel.bayesmeta
Liver transplant example dataGoralczykEtAl2011
JIA example dataHinksEtAl2010
COPD example dataKarnerEtAl2014
Kullback-Leibler divergence of two multivariate normal distributions.kldiv
Multiple sclerosis disability progression example dataNicholasEtAl2019
Compute normal mixturesnormalmixture
Aspirin after myocardial infarction example dataPeto1980
Generate summary plots for a 'bayesmeta' object.plot.bayesmeta
Posterior predictive p-valuespppvalue
Heterogeneity priors for continuous outcomes (standardized mean differences) as proposed by Rhodes et al. (2015).RhodesEtAlParameters RhodesEtAlPrior
Aspirin during pregnancy example dataRobergeEtAl2017
8-schools example dataRubin1981
Historical variance example dataSchmidliEtAl2017
Postoperative complication odds example dataSidikJonkman2007
Artificial insemination of cows example dataSnedecorCochran
Summarizing a 'bmr' object).print.summary.bmr summary.bmr
Illustrate conditional means of study-specific estimates as well as overall mean (or other linear combinations) as a function of heterogeneity.traceplot traceplot.bayesmeta traceplot.bmr traceplot.default
(Log-Normal) heterogeneity priors for binary outcomes as proposed by Turner et al. (2015).TurnerEtAlParameters TurnerEtAlPrior
Unit information standard deviationuisd uisd.default uisd.escalc
Illustrate the posterior mean weights for a 'bayesmeta' object.weightsplot weightsplot.bayesmeta weightsplot.default