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 <http://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.

Basic infrastructure and several algorithms for 1d-4d bin packing
    problem. This package provides a set of c-level classes and solvers for
    1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
    which is a wrapper over the c-level 4d bin packing problem solver.
    The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
    container loading problem, with an additional constraint on weight.
    Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
    with weight limit, the solver looks for an orthogonal packing solution
    such that minimizes the number of bins and maximize volume utilization.
    Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
    depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
    j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
    and weight limit w_j.
    The item can be rotated into any orthogonal direction, and no further
    restrictions implied.