A collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.

A model for the growth of self-limiting populations using three, four, or five parameter 
    functions, which have wide applications in a variety of fields.
    The dependent variable in a dynamical modeling could be the population size at time x, 
    where x is the independent variable. In the analysis of quantitative polymerase chain reaction 
    (qPCR), the dependent variable would be the fluorescence intensity and the independent variable 
    the cycle number. This package then would calculate the TWW cycle threshold.

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>).