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  "Description": "Implements a bias-aware framework for evidence synthesis\nin systematic reviews and health technology assessments, as\ndescribed in Kabali (2025) <doi:10.1111/jep.70272>. The package\nmodels study-level effect estimates by explicitly accounting\nfor multiple sources of bias through prior distributions and\npropagates uncertainty using posterior simulation. Evidence\nacross studies is combined using posterior mixture\ndistributions rather than a single pooled likelihood, enabling\nprobabilistic inference on clinically or policy-relevant\nthresholds. The methods are designed to support transparent\ndecision-making when study relevance and bias vary across the\nevidence base.",
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