Implements a parametric decision-theoretic framework for optimal
    diagnostic cutoff selection under the family of scale mixtures of skew-normal
    (SMSN) distributions, including the skew-normal (SN) and skew-t (ST) models
    as special cases. The optimal cutoff is defined by minimising a weighted
    misclassification risk that incorporates disease prevalence and asymmetric
    costs, leading to a likelihood-ratio equation that generalises the Youden
    criterion. Under a monotone likelihood ratio condition, existence, uniqueness,
    and global optimality of the cutoff are established. Asymptotic normality and
    a closed-form plug-in variance estimator are provided via the implicit function
    theorem and the multivariate delta method. Tools for model fitting, cutoff
    estimation, confidence intervals, the local identifiability diagnostic, and
    Monte Carlo simulation are included. The methodology is described in
    de Paula, Mouriño, and Dias Domingues (2026)
    <doi:10.48550/arXiv.2605.07829>.

Provides estimations of the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).

Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.

Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.

Implementation of the SMuRF algorithm of Devriendt et al. (2021) <doi:10.1016/j.insmatheco.2020.11.010> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.

Testing the mediation effect of multiple SNPs on an outcome through a mediator.