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.

Provides utilities for processing Rd objects and files.
  Extract argument descriptions and other parts of the help pages of
  functions.

Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search. Instead of giving the single best model, the final output is an 
    ensemble of Gradient Boosted Trees constructed via the method of ensemble 
    selection.

Plot density and distribution functions with automatic selection of
       suitable regions. Numerically invert (compute quantiles) distribution
       functions. Simulate real and complex numbers from distributions of their
       magnitude and arguments. Optionally, the magnitudes and/or arguments may
       be fixed in almost arbitrary ways. Create polynomials from roots given in
       Cartesian or polar form. Small programming utilities: check if an object
       is identical to NA, count positional arguments in a call, set
       intersection of more than two sets, check if an argument is unnamed,
       compute the graph of S4 classes in packages.