Random sampling from distributions with user-specified population covariance matrix. Marginal information may be fully
        specified, for which the package implements the VITA (VIne-To-Anything) algorithm
        Grønneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>.  See also Grønneberg, Foldnes and Marcoulides (2022) <doi:10.18637/jss.v102.i03>.
        Alternatively, marginal skewness and kurtosis may be specified, for which the package
        implements the IG (independent generator) and PLSIM (piecewise linear) algorithms, see Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274> and
        Foldnes and Grønneberg (2021) <doi:10.1080/10705511.2021.1949323>, respectively.

Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance
  matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of 
  covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix
  tests use C++ to speed performance and allow larger data sets.

Covariance is of universal prevalence across various disciplines within statistics.
    We provide a rich collection of geometric and inferential tools for convenient analysis of
    covariance structures, topics including distance measures, mean covariance estimator,
    covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
    For an introduction to covariance in multivariate statistical analysis,
    see Schervish (1987) <doi:10.1214/ss/1177013111>.

Dissects a package environment or 'covr' coverage object in order to cross
    reference tested code with the lines that are evaluated, as well as linking
    those evaluated lines to the documentation that they are described within.
    Connecting these three pieces of information provides a mechanism of 
	linking tests to documented behaviors.

Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.