Functional Multivariable Mendelian Randomization
Two-sample joint multivariable FMR (internal)
Automatic Multivariable Functional MR with joint estimation (internal)
Control function for logit model
Two-Sample Separate Univariable Functional MR
Two-Sample Joint Multivariable Functional MR
Get true effect function for simulation
Get true shape values for simulation
Generate multi-exposure mediation data with genetic instruments
Generate multi-exposure data with genetic instruments
Generate outcome from exposures
GMM estimation for continuous outcome
Two-sample GMM
Calculate F-statistics and Q-statistic for instrument strength (intern...
Separate Univariable Functional Mendelian Randomization
mvfmr: Multivariable Functional Mendelian Randomization
Joint Multivariable Functional Mendelian Randomization
Separate univariable two-sample FMR (internal)
Separate univariable functional MR estimation (internal)
Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.