Automates delta log-normal boosted regression tree abundance
    prediction. Loops through parameters provided (LR (learning rate), TC
    (tree complexity), BF (bag fraction)), chooses best, simplifies, &
    generates line, dot & bar plots, & outputs these & predictions & a
    report, makes predicted abundance maps, and Unrepresentativeness
    surfaces.  Package core built around 'gbm' (gradient boosting machine)
    functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020
    & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham
    & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to
    Elith/Leathwick/Hastie 2008 'Working Guide'
    <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3.
    See <https://www.simondedman.com/> for published guides and papers
    using this package.

Writes SAS code to get predicted values from every tree of a gbm.object.

Estimation and analysis of group-based multivariate trajectory models (Nagin, 2018 <doi:10.1177/0962280216673085>; Magrini, 2022 <doi:10.1007/s10182-022-00437-9>). The package implements an Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing values, and provides several functionalities for prediction and graphical representation.

Provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including:
    - PSO-Default, based on Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, "Particle Swarm Optimization";
    - PSO-Quantum, based on Sun, Xu, and Feng (2004) <doi:10.1109/ICCIS.2004.1460396>, "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization";
    - PSO-Dexp, based on Stehlík et al. (2024) <doi:10.1016/j.asoc.2024.111913>, "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics";
    - and PSO-GO.