NEWS

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metaGLMM 1.0.0 (2026-09-12)

Initial CRAN release

• Provides likelihood-based meta-analysis through generalized linear mixed-effects models using aggregate data.
• Supports covariate adjustment and non-normal response families, with computationally efficient likelihood evaluation.
• Includes likelihood-based confidence intervals and the fast and quasi-Monte Carlo integration methods described by Hanada and Sugimoto (2026).
• Draws selected Wald, profile, SBC, and plug-in prediction intervals as forest-plot diamonds, adds numeric annotations, and automatically reserves space for long labels and headers.
• Lets forest(fit) automatically use safe study-level data and a uniquely identified treatment coefficient, with SBC as the default when available.
• Expands the binary, Poisson, and Gaussian vignettes through forest-plot workflows and replaces the custom Gaussian vignette example with an overdispersed negative-binomial rate model.
• Supports profile and simple Bartlett-corrected confidence intervals for custom families when heterogeneity is estimated and an appropriate study-level sampling variance is supplied through vi.