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>.

Diagnose, visualize, and aggregate event report 
    level data to the event level. Users provide an event report level 
    dataset, specify their aggregation rules, and the package produces a
    dataset aggregated at the event level. Also includes the Modes and 
    Agents of Election-Related Violence in Côte d'Ivoire and Kenya (MAVERICK) dataset,
    an event report level dataset that records all documented instances of 
    electoral violence from the first multiparty election to 2022 in 
    Côte d'Ivoire (1995-2022) and Kenya (1992-2022).
    For more details see van Baalen and Höglund (2026)
    <doi:10.1093/isq/sqag014>. Users of the enclosed MAVERICK dataset
    should also cite van Baalen and Höglund (2026)
    <doi:10.1093/jopres/xjaf012>.

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>.