A modular simulation engine for a wide range of stochastic
    processes. Provides exact and approximate simulation methods for Poisson
    processes (homogeneous and inhomogeneous), Brownian motion (standard,
    drifted, and bridge), discrete- and continuous-time Markov chains,
    birth-death processes, the Yule pure-birth process, infinitesimal
    generator matrix utilities, Markovian queuing systems (M/M/1, M/M/c,
    M/M/c/K) with exact steady-state statistics, Levy processes (gamma,
    normal inverse Gaussian, variance-gamma, alpha-stable), Merton
    jump-diffusion models, Hawkes self-exciting processes, geometric Brownian
    motion, and Ornstein-Uhlenbeck mean-reverting diffusions. Includes
    variance reduction techniques (antithetic variates, control variates,
    importance sampling, stratified sampling), parallel simulation via the
    'future' framework, rare-event simulation (cross-entropy and multilevel
    splitting), path visualisation, and summary statistics. Methods are based
    on Glasserman (2003) <doi:10.1007/978-0-387-21617-1>, Asmussen & Glynn
    (2007) <doi:10.1007/978-0-387-69033-9>, Norris (1997)
    <doi:10.1017/CBO9780511810633>, and Kleinrock (1975, ISBN:0471491101).

Flexible stochastic tree ensemble software. 
    Robust implementations of Bayesian Additive Regression Trees (BART) 
    (Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285>) 
    for supervised learning and Bayesian Causal Forests (BCF) 
    (Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195>) 
    for causal inference. Enables model serialization and parallel sampling 
    and provides a low-level interface for custom stochastic forest samplers.
    Includes the grow-from-root algorithm for accelerated forest sampling 
    (He and Hahn (2021) <doi:10.1080/01621459.2021.1942012>), 
    a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) <doi:10.1080/01621459.2020.1813587>), 
    and the cloglog BART model of Alam and Linero (2025) <doi:10.48550/arXiv.2502.00606> for ordinal outcomes.