person(given = "Wolfgang", family = "Viechtbauer", role = c("aut","cre"), email = "wvb@metafor-project.org", comment = c(ORCID = "0000-0003-3463-4063"))
R (>= 4.0.0), methods, Matrix, metadat, numDeriv
stats, utils, graphics, grDevices, nlme, mathjaxr, pbapply,
digest
lme4, pracma, minqa, nloptr, dfoptim, ucminf, lbfgsb3c,
subplex, BB, Rsolnp, alabama, optimParallel, optimx,
CompQuadForm, mvtnorm, BiasedUrn, Epi, survival, GLMMadaptive,
glmmTMB, car, multcomp, gsl, sp, ape, boot, clubSandwich,
crayon, R.rsp, testthat, rmarkdown, wildmeta, emmeans,
estmeansd, metaBLUE, rstudioapi, glmulti, MuMIn, mice, Amelia,
calculus
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, 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>.
Wolfgang Viechtbauer [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-3463-4063>)