Provides functions and a workflow to easily and powerfully calculating specificity, sensitivity and ROC curves of biomarkers combinations. Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) <doi:10.1038/srep45477> and further described in Bombaci & Rossi (2019) <doi:10.1007/978-1-4939-9164-8_16>, and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. <doi:10.1101/2022.01.17.476603>.

Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.

Use frequentist and Bayesian methods to estimate parameters from a
  binary outcome misclassification model. These methods correct for the problem
  of "label switching" by assuming that the sum of outcome sensitivity and 
  specificity is at least 1. A description of the analysis methods is
  available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

Bayesian regression whose response data are integer-valued vectors subject to combinatorial constraints in the form of Ay<=b. Implements the Metropolis-Hastings-within-Gibbs sampler of Zheng et al. (2026+) <doi:10.48550/arXiv.2504.11630>, an unconstrained probit baseline for comparison, benchmarking helpers, MCMC and regression diagnostics (effective sample sizes, split-Rhat, structured reports), plotting methods (trace, autocorrelation, violin, effective-sample-size, sampling-efficiency, residual heat map), and utilities for constraint validation (total unimodularity, feasibility) and data simulation.

Best subset selection in generalised linear models via continuous
    optimisation. Reformulates the NP-hard discrete subset selection problem as
    a continuous optimisation over the hypercube [0,1]^p, solved via a
    Frank-Wolfe homotopy algorithm with closed-form ridge inner solves.
    Supports linear (Gaussian), binary logistic, and multinomial regression.
    For methodological details see Moka, Liquet, Zhu and Muller (2024)
    <doi:10.1007/s11222-024-10387-8> and Mathur, Liquet, Muller and Moka
    (2026) <doi:10.48550/arXiv.2603.21952>.