drmeta: Design-Robust Meta-Analysis via Variance-Function Models

Implements Design-Robust Meta-Analysis (DR-Meta), a variance-function random-effects framework in which between-study heterogeneity is modelled as a function of a study-level design robustness index, allowing heterogeneity to depend systematically on study quality or design strength rather than being treated as a single nuisance parameter. The package provides profiled restricted maximum likelihood (REML) estimation of the overall effect and variance-function parameters, study-specific weights, heterogeneity diagnostics (tau-squared, I-squared), influence and leave-one-out analysis, and graphical tools including forest plots and influence plots. The DR-Meta framework nests classical fixed-effects and standard random-effects meta-analysis as special cases, making it a strict generalisation of existing approaches.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	grDevices, graphics, stats, utils
Suggests:	ggplot2 (≥ 3.4.0), knitr, metafor (≥ 4.0.0), rmarkdown, testthat (≥ 3.0.0)
Published:	2026-04-08
DOI:	10.32614/CRAN.package.drmeta
Author:	Subir Hait [aut, cre]
Maintainer:	Subir Hait <haitsubi at msu.edu>
BugReports:	https://github.com/causalfragility-lab/drmeta/issues
License:	MIT + file LICENSE
URL:	https://github.com/causalfragility-lab/drmeta
NeedsCompilation:	no
Materials:	README
CRAN checks:	drmeta results

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