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>.

Provides methods to analyze micro-randomized trials (MRTs) with binary treatment options.  Supports four types of analyses: (1) proximal causal excursion effects, including weighted and centered least squares (WCLS) for continuous proximal outcomes by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274> and the estimator for marginal excursion effect (EMEE) for binary proximal outcomes by Qian et al. (2021) <doi:10.1093/biomet/asaa070>; (2) distal causal excursion effects (DCEE) for continuous distal outcomes using a two-stage estimator by Qian (2025) <doi:10.1093/biomtc/ujaf134>; (3) mediated causal excursion effects (MCEE) for continuous distal outcomes, estimating natural direct and indirect excursion effects in the presence of time-varying mediators by Qian (2025) <doi:10.48550/arXiv.2506.20027>; and (4) standardized proximal effect size estimation for continuous proximal outcomes, generalizing the approach in Luers et al. (2019) <doi:10.1007/s11121-017-0862-5> to allow adjustment for baseline and time-varying covariates for improved efficiency.