Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).

Provides readers for easy and consistent importing of
    Mouse Genome Informatics (MGI) report files:
    <https://www.informatics.jax.org/downloads/reports/index.html>. These data
    are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ,
    Mouse Genome Informatics Group (2024) <doi:10.1093/genetics/iyae031>.

An aggressive dimensionality reduction and network estimation
    technique for a high-dimensional Gaussian graphical model (GGM). Please
    refer to: Efficient Dimensionality Reduction for High-Dimensional Network
    Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
    The 31st International Conference on Machine Learning, 2014, p. 1953--1961.

Inference of Multiscale graphical models with neighborhood
    selection approach.  The method is based on solving a convex
    optimization problem combining a Lasso and fused-group Lasso
    penalties.  This allows to infer simultaneously a conditional
    independence graph and a clustering partition. The optimization is
    based on the Continuation with Nesterov smoothing in a
    Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
    <doi:10.1109/TMI.2018.2829802> implemented in python.

Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.