Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.

A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.

Provides functions to load and analyze three open Electronic
    Health Records (EHRs) datasets of patients diagnosed with
    glioblastoma, previously released under the Creative Common
    Attribution 4.0 International (CC BY 4.0) license.  Users can generate
    basic descriptive statistics, frequency tables and save descriptive
    summary tables, as well as create and export univariate or bivariate
    plots.  The package is designed to work with the included datasets and
    to facilitate quick exploratory data analysis and reporting.  More
    information about these three datasets of EHRs of patients with
    glioblastoma can be found in this article: Gabriel Cerono, Ombretta
    Melaiu, and Davide Chicco, 'Clinical feature ranking based on ensemble
    machine learning reveals top survival factors for glioblastoma
    multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March
    2024).  <doi:10.1007/s41666-023-00138-1>.