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.

Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. 
    The inference procedure handles cross-sectional data sequentially: 
    (i) updating the parameter estimate with each incoming "new observation", 
    (ii) aggregating it as a Polyak-Ruppert average, and 
    (iii) computing an asymptotically pivotal statistic for inference through random scaling. 
    The methodology used in the 'SGDinference' package is described in detail in the following papers: 
    (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling".
    (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".

Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a 'path' of candidate models from which model selection can be performed. This 'slow brewing' approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.