Functions for analysis of rate changes in sequential events.

Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations.

Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) <doi:10.1186/s12874-016-0117-3>.

Implements event extraction and early classification of events in data streams in R. 
    It has the functionality to generate 2-dimensional data streams with events belonging to 
    2 classes. These events can be extracted and features computed. The event features extracted
    from incomplete-events can be classified using a partial-observations-classifier 
    (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.

Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) <doi:10.3386/w29170>. Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) <doi:10.1257/aer.20180609>.

Implements the hybrid framework for event prediction described in Fang & Zheng (2011, <doi:10.1016/j.cct.2011.05.013>). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential.

Provides several confidence interval and testing procedures using
    event-specific win ratios for semi-competing risks data with non-terminal
    and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). 
    Compared with conventional methods for survival data, these procedures are 
    designed to utilize more data for improved inference procedures with 
    semi-competing risks data. The event-specific win ratios were introduced in 
    Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, 
    the event-specific win ratios and confidence intervals are obtained for each 
    event type, and several testing procedures are developed for the global null 
    of no treatment effect on either terminal or non-terminal events. Furthermore,
    a test of proportional hazard assumptions, under which the event-specific win 
    ratios converge to the hazard ratios, and a test of equal hazard ratios are 
    provided. For summarizing the treatment effect on all events, confidence 
    intervals for linear combinations of the event-specific win ratios are available
    using pre-determined or data-driven weights. Asymptotic properties of these 
    inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). 
    Also, transformations are used to yield better control of the type one error 
    rates for moderately sized data sets.