Wrapper functions around the Facebook Marketing 'API' to create,
    read, update and delete custom audiences, images, campaigns, ad sets, ads and
    related content.

A formula-based wrapper around 'glmnet' that brings the
    'glm()'-compatible modeling workflow to regularized generalized
    linear models. Training-time 'terms', 'xlevels', and 'contrasts'
    are stored on the fit object and reused at predict time, so the
    design matrix is reconstructed consistently across sessions.
    Complete-case bookkeeping is exposed via 'nobs_info', and linearly
    dependent columns are detected by a QR pivot and reported as 'NA'
    in 'coef()' and 'summary()' (the 'stats::glm()' convention),
    distinguishing "not identifiable" from "shrunk to zero by the
    penalty". Novel factor levels at predict time raise the same error
    'stats::predict.glm()' does by default, with
    'on_new_levels = "na"' as a production-style opt-in. Accepts
    character family strings ('gaussian', 'binomial', 'poisson',
    'cox', 'multinomial', 'mgaussian') and any 'glm' family object the
    underlying 'glmnet' itself accepts, including 'Gamma' and
    fixed-theta negative binomial via 'MASS::negative.binomial'.

Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.

Provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available.