Meta-analytical models are typically formulated as a mixed-effects model where the sampling variances of the effect sizes are treated as known. In principle, such models could be fitted with standard mixed-modelling software such as the glmmTMB R package. This general-purpose package for generalized linear mixed models (GLMMs) provides flexibility in distributions and random effect covariance structures through the Template Model Builder (TMB). However, incorporating known sampling variances in the conventional inverse-variance formulation of meta-analysis was previously not easily accomplished in glmmTMB. Here, we introduce equalto, a new covariance structure in glmmTMB that allows users to supply a known sampling error variance-covariance matrix when fitting meta-analytic models. This enables explicit modelling of heteroscedasticity and dependence among sampling errors. The new implementation provides an alternative way to fit meta-analytic models, convenient for users already familiar with glmmTMB. Using simulations, we show that the new implementation produces model estimates identical to those from the established metafor package and illustrate its applicability with published meta-analyses in medicine, evolutionary ecology, and the social sciences. Further, this novel implementation in glmmTMB supports more flexible modelling of meta-analytical data, expanding the R toolkit available for evidence synthesis.

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