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  "Title": "Functional Multivariable Mendelian Randomization",
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  "Description": "Implements Multivariable Functional Mendelian\nRandomization (MV-FMR) to estimate time-varying causal effects\nof multiple longitudinal exposures on health outcomes. Extends\nunivariable functional Mendelian Randomisation (MR) (Tian et\nal., 2024 <doi:10.1002/sim.10222>) to the multivariable\nsetting, enabling joint estimation of multiple time-varying\nexposures with pleiotropy and mediation scenarios. Key features\ninclude: (1) data-driven cross-validation for basis component\nselection, (2) handling of mediation pathways between\nexposures, (3) support for both continuous and binary outcomes\nusing Generalized Method of Moments (GMM) and control function\napproaches, (4) one-sample and two-sample MR designs, (5)\nbootstrap inference and instrument diagnostics including\nQ-statistics for overidentification testing. Methods are\ndescribed in Fontana et al. (2025)\n<doi:10.48550/arXiv.2512.19064>.",
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