Nonparametric maximum likelihood estimation methods
    for random coefficient binary response  models and some related
    functionality for sequential processing of hyperplane arrangements.
    See J. Gu and R. Koenker (2020) <DOI:10.1080/01621459.2020.1802284>.

Tools for optimal subset matching of treated units
	and control units in observational studies, with support
	for refined covariate balance constraints, (including
	fine and near-fine balance as special cases). A close 
	relative is the 'rcbalance' package.  See Pimentel, 
	et al.(2015) <doi:10.1080/01621459.2014.997879>
	and Pimentel and Kelz (2020) 
	<doi:10.1080/01621459.2020.1720693>. The rrelaxiv 
	package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.

Functions to implement the parametric and non-parametric bootstrap 
    confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.

Fit Class Cover Catch Digraph Classification models that can be 
    used in machine learning. Pure and proper and random walk approaches are 
    available. Methods are explained in Priebe et al. (2001) 
    <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) 
    <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) 
    <doi:10.48550/arXiv.1904.04564>.

The handling of an API key (misnomer for password) for protected
    data can be difficult. This package provides secure convenience functions for 
    entering / handling API keys and pulling data directly into memory. By default
    it will load from REDCap instances, but other sources are injectable via
    inversion of control.

The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021).

R interface to (some of) cddlib
    (<https://github.com/cddlib/cddlib>).
    Converts back and forth between two representations of a convex polytope:
    as solution of a set of linear equalities and inequalities and as
    convex hull of set of points and rays.
    Also does linear programming and redundant generator elimination
    (for example, convex hull in n dimensions).  All functions can use exact
    infinite-precision rational arithmetic.