Stop signal task  data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.

Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>.

Provides a streamlined and user-friendly framework
    for simulating data in state space models,
    particularly when the number of subjects/units (n) exceeds one,
    a scenario commonly encountered in social and behavioral sciences.
    For an introduction to state space models in social and behavioral sciences,
    refer to Chow, Ho, Hamaker, and Dolan (2010) <doi:10.1080/10705511003661553>.

Simulates data sets in order to explore modeling
    techniques or better understand data generating processes. The user
    specifies a set of relationships between covariates, and generates
    data based on these specifications. The final data sets can represent
    data from randomized control trials, repeated measure (longitudinal)
    designs, and cluster randomized trials. Missingness can be generated
    using various mechanisms (MCAR, MAR, NMAR).

Simulate survival times from standard parametric survival
    distributions (exponential, Weibull, Gompertz), 2-component mixture
    distributions, or a user-defined hazard, log hazard, cumulative hazard,
    or log cumulative hazard function. Baseline covariates can be included
    under a proportional hazards assumption.
    Time dependent effects (i.e. non-proportional hazards) can be included by
    interacting covariates with linear time or a user-defined function of time.
    Clustered event times are also accommodated.
    The 2-component mixture distributions can allow for a variety of flexible
    baseline hazard functions reflecting those seen in practice.
    If the user wishes to provide a user-defined
    hazard or log hazard function then this is possible, and the resulting
    cumulative hazard function does not need to have a closed-form solution.
    For details see the supporting paper <doi:10.18637/jss.v097.i03>.
    Note that this package is modelled on the 'survsim' package available in
    the 'Stata' software (see Crowther and Lambert (2012)
    <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).