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.

Fused lasso method to cluster and estimate regression coefficients
    of the same covariate across different data sets when a large number of
    independent data sets are combined. Package supports Gaussian, binomial,
    Poisson and Cox PH models.

Meta-analysis of generalized additive
    models and generalized additive mixed models. A typical use case is
    when data cannot be shared across locations, and an overall meta-analytic
    fit is sought. 'metagam' provides functionality for removing individual
    participant data from models computed using the 'mgcv' and 'gamm4' packages such
    that the model objects can be shared without exposing individual data.
    Furthermore, methods for meta-analysing these fits are provided. The implemented
    methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>,
    extending previous works by Schwartz and Zanobetti (2000)
    and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.