A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic 
  Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the 
  generalization performance of stochastic gradient descent and at least as fast 
  convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation
  is provided based on Defazio et al (2020) <doi:10.48550/arXiv.2101.11075>.

Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without
              significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data
              manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 
              'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional
              aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' 
              conversion to 'data.table'.

This system allows one to model a multi-variate, multi-response
    problem with interaction effects. It combines the usual squared error loss
    for the multi-response problem with some penalty terms to encourage
    responses that correlate to form groups and also allow for modeling main and
    interaction effects that exit within the covariates.
    The optimization method employed is the Alternating Direction Method of
    Multipliers (ADMM). The implementation is based on the methodology
    presented on Quachie Asenso, T., & Zucknick, M. (2023)
    <doi:10.48550/arXiv.2303.11155>.

An object that supports automatic differentiation
    of matrix- and multidimensional-valued functions with 
    respect to multidimensional independent variables. 
    Automatic differentiation is via 'forward accumulation'.

Tools for the analysis of population differences
    using the Major Histocompatibility Complex (MHC) genotypes of samples
    having a variable number of alleles (1-4) recorded for each
    individual.  A hierarchical Dirichlet-Multinomial model on the
    genotype counts is used to pool small samples from multiple
    populations for pairwise tests of equality.  Bayesian inference is
    implemented via the 'rstan' package.  Bootstrapped and posterior
    p-values are provided for chi-squared and likelihood ratio tests of
    equal genotype probabilities.