Description: A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.

Imports: stats, utils, graphics, grDevices, nlme

Authors@R: person(given = "Wolfgang", family = "Viechtbauer", role = c("aut","cre"), email = "wvb@metafor-project.org", comment = c(ORCID = "0000-0003-3463-4063"))
mvtnorm, Formula, BiasedUrn, Epi, multcomp, gsl, sp, survival,
boot, MASS, crayon, R.rsp, testthat, rmarkdown

Suggests: lme4, numDeriv, minqa, nloptr, dfoptim, ucminf,
optimParallel, CompQuadForm, mvtnorm, BiasedUrn, Epi, survival,
GLMMadaptive, multcomp, gsl, sp, boot, crayon, R.rsp, testthat,

Imports: stats, utils, graphics, grDevices, nlme, mathjaxr, pbapply
GLMMadaptive, multcomp, gsl, sp, ape, boot, crayon, R.rsp,
Description: A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.