Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).

Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.

Provides estimation and leave-one-cluster-out jackknife standard errors
    for four longitudinal cluster-randomized trial estimands: horizontal individual
    average treatment effect (h-iATE), horizontal cluster average treatment effect
    (h-cATE), vertical individual average treatment effect (v-iATE), and vertical
    cluster-period average treatment effect (v-cATE), using unadjusted and augmented
    (model-robust standardization) estimators. The working model may be fit using
    linear mixed models for continuous outcomes or generalized estimating equations
    and generalized linear mixed models for binary outcomes. Period inclusion for
    aggregation is determined automatically: only periods with both treated and
    control clusters are included in the construction of the marginal means and
    treatment effect contrasts. See Fang et al. (2025) <doi:10.48550/arXiv.2507.17190>.