Depends: R (>= 4.0.0), methods, Matrix, metadat
Suggests: lme4, numDeriv, pracma, minqa, nloptr, dfoptim, ucminf,
lbfgsb3c, subplex, BB, optimParallel, CompQuadForm, mvtnorm,
BiasedUrn, Epi, survival, GLMMadaptive, glmmTMB, multcomp, gsl,
sp, ape, boot, clubSandwich, crayon, R.rsp, testthat,
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 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>.

lbfgsb3c, subplex, BB, Rsolnp, alabama, optimParallel,
CompQuadForm, mvtnorm, BiasedUrn, Epi, survival, GLMMadaptive,
glmmTMB, multcomp, gsl, sp, ape, boot, clubSandwich, crayon,

Title: Exploring Heterogeneity in Meta-Analysis using Random Forests
Maintainer: Caspar J. van Lissa <c.j.vanlissa@gmail.com>
Description: A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Similar research questions are studied in different laboratories, using different methods, instruments, and samples. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators, or to investigate interactions between moderators, and non-linear effects. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
Depends: R (>= 3.4.1), edarf, ggplot2, metafor, ranger, reshape2