Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. 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. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). 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.

Implements metafrontier production function models for
    estimating technical efficiencies and technology gaps for groups of
    firms that face different restrictions of a common underlying
    metatechnology (group-specific technologies in the sense of Battese,
    Rao, and O'Donnell, 2004). Supports both stochastic frontier
    analysis (SFA) and data envelopment analysis (DEA) based
    metafrontiers. Includes the deterministic metafrontier of Battese,
    Rao, and O'Donnell (2004) <doi:10.1023/B:PROD.0000012454.06094.29>,
    the stochastic metafrontier of Huang, Huang, and Liu (2014)
    <doi:10.1007/s11123-014-0402-2>, and the metafrontier Malmquist
    productivity index of O'Donnell, Rao, and Battese (2008)
    <doi:10.1007/s00181-007-0119-4>. The deterministic metafrontier can
    be identified by either the minimum sum of absolute deviations (LP)
    or the minimum sum of squared deviations (QP) criterion. Additional
    features include panel SFA with time-varying inefficiency, bootstrap
    confidence intervals for technology gap ratios, a DEA poolability
    permutation test, latent class metafrontier estimation via the EM
    algorithm, Murphy-Topel corrected standard errors, convergence
    diagnostics, import of pre-fitted models from external estimation
    engines ('sfaR', 'frontier', 'Benchmarking'), and 'ggplot2'
    visualisation methods.