Provides several confidence interval and testing procedures, based on either 
    semiparametric (using event-specific win ratios) or nonparametric measures, 
    including the ratio of integrated cumulative hazard (RICH) and the ratio of 
    integrated transformed cumulative hazard (RITCH), for treatment effect inference 
    with terminal and non-terminal events under competing risks. The semiparametric 
    results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the 
    nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). 
    For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 
    <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), 
    Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 
    <doi:10.1093/biostatistics/kxv032>) are included. The package also supports 
    univariate survival analysis with a single event. In this package, effect size 
    estimates and confidence intervals are obtained for each event type, and several 
    testing procedures are implemented for the global null hypothesis of no treatment 
    effect on either terminal or non-terminal events. Furthermore, a test of proportional 
    hazards assumptions, under which the event-specific win ratios converge to hazard 
    ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment 
    effect across all events, confidence intervals for linear combinations of the 
    event-specific win ratios, RICH, or RITCH are available using pre-determined or 
    data-driven weights. Asymptotic properties of these inference procedures are 
    discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 
    <doi:10.1002/sim.70205>).

Efficient estimation of the population-level causal effects of
    stochastic interventions on a continuous-valued exposure. Both one-step and
    targeted minimum loss estimators are implemented for the counterfactual mean
    value of an outcome of interest under an additive modified treatment policy,
    a stochastic intervention that may depend on the natural value of the
    exposure. To accommodate settings with outcome-dependent two-phase
    sampling, procedures incorporating inverse probability of censoring
    weighting are provided to facilitate the construction of inefficient and
    efficient one-step and targeted minimum loss estimators.  The causal
    parameter and its estimation were first described by Díaz and van der Laan
    (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust
    estimation procedure and its application to data from two-phase sampling
    designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
    and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
    implementation is described in NS Hejazi and DC Benkeser (2020)
    <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be
    enhanced through the Super Learner ensemble model in 'sl3', available for
    download from GitHub using 'remotes::install_github("tlverse/sl3")'.