Package: bayesmeta 3.4

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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bayesmeta.pdf |bayesmeta.html✨
bayesmeta/json (API)

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

Peer review:

Datasets:

On CRAN:

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

5.24 score 3 stars 1 packages 72 scripts 731 downloads 9 mentions 68 exports 13 dependencies

Last updated 9 months agofrom:e7eaba2af6. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-winNOTENov 13 2024
R-4.5-linuxNOTENov 13 2024
R-4.4-winOKNov 13 2024
R-4.4-macOKNov 13 2024
R-4.3-winOKNov 13 2024
R-4.3-macOKNov 13 2024

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

Dependencies:abindbackportscheckmateforestplotlatticemathjaxrMatrixmetadatmetaformvtnormnlmenumDerivpbapply

Bayesian random-effects meta-analysis using the bayesmeta R package

Rendered fromRoever2020-bayesmeta.pdf.asisusingR.rsp::asison Nov 13 2024.

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

An introduction to meta-analysis using the bayesmeta package

Christian Röver

Rendered frombayesmeta.Rmdusingknitr::rmarkdownon Nov 13 2024.

Last update: 2021-08-12
Started: 2015-12-16

Using the bayesmeta R package for Bayesian random-effects meta-regression

Rendered fromRoeverFriede2023-UsingBayesmetaForMetaRegression.pdf.asisusingR.rsp::asison Nov 13 2024.

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

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