Depends: R (>= 3.0.0), stats, Formula, Matrix
Suggests: lme4, numDeriv, polycor, BiasedUrn, survival, Epi, minqa,

Authors@R: person('Wolfgang', 'Viechtbauer', email = 'wvb@metafor-project.org', role = c('aut', 'cre'))
multcomp, plyr, igraph, testthat, CompQuadForm, R.rsp, nloptr

Authors@R: person('Wolfgang', 'Viechtbauer', email = 'wvb@metafor-project.org', role = c('aut','cre'))
Suggests: R.rsp, testthat, Formula, lme4, numDeriv, polycor, BiasedUrn,
survival, Epi, minqa, multcomp, plyr, igraph, CompQuadForm,

Suggests: lme4, numDeriv, minqa, nloptr, dfoptim, ucminf, CompQuadForm,
mvtnorm, Formula, R.rsp, testthat, BiasedUrn, survival, Epi,
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 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 (conditional) 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.

Authors@R: person("Wolfgang", "Viechtbauer", email = "wvb@metafor-project.org", role = c("aut","cre"))
Imports: stats, utils, graphics, grDevices, methods
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 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.