Simple 'SendGrid' Email API client for creating and sending emails. 
    For more information, visit the official 'SendGrid' Email API documentation: <https://sendgrid.com/en-us/solutions/email-api>.

Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.

Implements an extension of the Generalized Berk-Jones (GBJ) statistic for
    survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing 
    the association between a gene set and a time-to-event outcome with possible 
    adjustment on additional covariates. Detailed method is available at Villain L, Ferte T, 
    Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.

Sparse-group boosting to be used in conjunction with the 'mboost' for modeling grouped data.
    Applicable to all sparse-group lasso type problems where within-group and between-group sparsity is desired.
    Interprets and visualizes individual variables and groups.

A fast and flexible set of tools for large scale estimation. It
    features many stochastic gradient methods, built-in models, visualization
    tools, automated hyperparameter tuning, model checking, interval estimation,
    and convergence diagnostics.

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).