Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.

Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly
    functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations,
    copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'),
    microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). 
    See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.

Methods for learning causal relationships among a set of
    foreground variables X based on signals from a (potentially much
    larger) set of background variables Z, which are known non-descendants
    of X. The confounder blanket learner (CBL) uses sparse regression
    techniques to simultaneously perform many conditional independence
    tests, with complementary pairs stability selection to guarantee
    finite sample error control. CBL is sound and complete with respect to
    a so-called "lazy oracle", and works with both linear and nonlinear
    systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.

A function that facilitates fitting three types of models 
    for contrast-based Bayesian Network Meta Analysis.  The first model is that which
    is described in Lu and Ades (2006) <doi:10.1198/016214505000001302>.  The other two 
    models are based on a Bayesian nonparametric methods that permit ties when comparing 
    treatment or for a treatment effect to be exactly equal to zero. In addition to the 
    model fits, the package provides a summary of the interplay  between treatment 
    effects based on the procedure described in Barrientos, Page, and Lin (2023)
    <doi:10.48550/arXiv.2207.06561>.