CRAN Task View: Meta-Analysis

Maintainer:Michael Dewey, Wolfgang Viechtbauer
Contact:lists at
Contributions:Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.
Citation:Michael Dewey, Wolfgang Viechtbauer (2024). CRAN Task View: Meta-Analysis. Version 2024-06-04. URL
Installation:The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("MetaAnalysis", coreOnly = TRUE) installs all the core packages or ctv::update.views("MetaAnalysis") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.

This task view covers packages which include facilities for meta-analysis of summary statistics from primary studies. The task view does not consider the meta-analysis of individual participant data (IPD) which can be handled by any of the standard linear modelling functions but it does include some packages which offer special facilities for IPD.

The standard meta-analysis model is a form of weighted least squares and so any of the wide range of R packages providing weighted least squares would in principle be able to fit the model. The advantage of using a specialised package is that (a) it takes care of the small tweaks necessary (b) it provides a range of ancillary functions for displaying and investigating the model. Where the model is referred to below, it is this model which is meant.

Where summary statistics are not available, a meta-analysis of significance levels is possible. This is not completely unconnected with the problem of adjustment for multiple comparisons but the packages below which offer this, chiefly in the context of genetic data, also offer additional functionality.

Univariate meta-analysis

Preparing for meta-analysis

Fitting the model

Graphical methods

An extensive range of graphical procedures is available.

Investigating heterogeneity

Model criticism

Small study bias / Unobserved studies / Publication bias

The issue of whether small studies give different results from large studies can be addressed by visual examination of the funnel plots mentioned above. In addition:

A related issue in meta-analysis is the problem of unobserved studies and publication bias.

Other study designs

Meta-analysis of significance values

In all cases poolr considers correlated p-values in addition to independent. The others above do not.

Some methods are also provided in some of the genetics packages mentioned below.

Multivariate meta-analysis

Standard methods outlined above assume that the effect sizes are independent. This assumption may be violated in a number of ways: within each primary study multiple treatments may be compared to the same control, each primary study may report multiple endpoints or multiple assessments, or primary studies may be clustered for instance because they come from the same country or the same research team.

Meta-analysis of studies of diagnostic tests

A special case of multivariate meta-analysis is the case of summarising studies of diagnostic tests. This gives rise to a bivariate, binary meta-analysis with the within-study correlation assumed zero although the between-study correlation is estimated. This is an active area of research and a variety of methods are available including what is referred to here as Reitsma’s method and the hierarchical summary receiver operating characteristic (HSROC) method. In many situations these are equivalent.


Where suitable moderator variables are available they may be included using meta-regression. All these packages are mentioned above, this just draws that information together.

Individual participant data (IPD)

Where all studies provide individual participant data, software for the analysis of multi-centre trials or multi-centre cohort studies should prove adequate but is outside the scope of this task view (see the MixedModels task view for relevant packages). Other packages which provide facilities related to IPD are:

Network meta-analysis

Also known as multiple treatment comparison, this is a very active area of research and development. Note that some of the packages mentioned above under multivariate meta-analysis can also be used for network meta-analysis with appropriate setup.


There are a number of packages specialising in genetic data: catmap combines case-control and family study data, graphical facilities are provided, CPBayes uses a Bayesian approach to study cross-phenotype genetic associations, corrmeta performs correlated meta-analysis across multiple scans, gap combines p-values, getmstatistic quantifies systematic heterogeneity, getspres uses standardised predictive random effects to explore heterogeneity in genetic association meta-analyses, GMCM uses a Gaussian mixture copula model for high-throughput experiments, MendelianRandomization provides several methods for performing Mendelian randomisation analyses with summarised data, MetaIntegrator provides meta-analysis of gene expression data, metaMA provides meta-analysis of p-values or moderated effect sizes to find differentially expressed genes, metaRNASeq does meta-analysis from multiple RNA sequencing experiments, MetaSubtract uses leave-one-out methods to validate meta-GWAS results, ofGEM provides a method for identifying gene-environment interactions using meta-filtering, RobustRankAggreg provides methods for aggregating lists of genes, SPAtest combines association results, MetaSKAT provides for meta-analysis of the SKAT, and metaGE provides functions for a meta-analysis of genome-wide association studies for studying genotype x environment interactions.



CRAN packages

Core:meta, metafor.
Regular:aggregation, aides, altmeta, amanida, baggr, bamdit, BayesCombo, bayesmeta, BayesMultMeta, bipd, bnma, boot.heterogeneity, boutliers, brms, bspmma, CAMAN, catmap, CausalMetaR, CBnetworkMA, CIAAWconsensus, citationchaser, clubSandwich, coefa, CohensdpLibrary,, corrmeta, CoTiMA, CPBayes, crossnma, crwbmetareg, dfmeta, diagmeta, digitize, dmetatools, dosresmeta, DTAplots, ecoreg, effectsize, effsize, epiR, es.dif, esc, esci, estimraw, estmeansd, EValue, EvidenceSynthesis, forestly, forestmodel, forestplot, forestploter, forplo, fsn, gap, gemtc, GENMETA, getmstatistic, getspres, GMCM, harmonicmeanp, heterometa, jarbes, joint.Cox, juicr, KenSyn, MAd, mada, MBNMAdose, MBNMAtime, mc.heterogeneity, MendelianRandomization, meta.shrinkage, meta4diag, MetaAnalyser, metabias, metaBLUE, metaBMA, metabolic, metabup, metacart, metaconfoundr, metadat, metaDigitise, metaforest, metafuse, metagam, metaGE, metagear, metainc, MetaIntegration, MetaIntegrator, metaLik, metaMA, metamedian, metamicrobiomeR, metamisc, metansue, metap, metapack, metaplus, metapower, metapro, metarep, metaRNASeq, metaSEM, metasens, MetaSKAT, MetaStan, MetaSubtract, metaSurvival, metatest, metaumbrella, MetaUtility, metavcov, metaviz, metawho, miniMeta, mixmeta, MOTE, multibiasmeta, multinma, mvmeta, MVPBT, mvtmeta, netmeta, NMA, nmadb, NMADiagT, nmaINLA, NMAoutlier, nmaplateplot, nmarank, nmathresh, ofGEM, OssaNMA, pcnetmeta, pema, phacking, pimeta, PINMA, POMADE, poolr, PRISMA2020, PRISMAstatement, psychmeta, psymetadata, PublicationBias, publipha, puniform, ra4bayesmeta, RandMeta, rankinma, ratesci, RBesT, RcmdrPlugin.EZR, RcmdrPlugin.MA, RcmdrPlugin.RMTCJags, reappraised, rema, remaCor, revtools, rma.exact, rmeta, rnmamod, RoBMA, robumeta, RobustBayesianCopas, robustmeta, RobustRankAggreg, robvis, RTSA, SCMA, selectMeta, SingleCaseES, smd, SPAtest, ssifs, TFisher, vcmeta, viscomp, weightr, wildmeta, xmeta.
Archived:gmeta, metaRMST, mmeta.

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