Matrix-variate covariance estimation via the Kronecker-core decomposition. Computes the Kronecker and core covariance matrices corresponding to an arbitrary covariance matrix, and provides an empirical Bayes covariance estimator that adaptively shrinks towards the space of separable covariance matrices. For details, see Hoff, McCormack and Zhang (2022) <arXiv:2207.12484> "Core Shrinkage Covariance Estimation for Matrix-variate data".

Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <https://about.codecov.io> or
    'Coveralls' <https://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.

The cov.nnve() function implements robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.

Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.

Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) =
    C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    Aston et al. (2017) <doi:10.1214/16-AOS1495> <doi:10.48550/arXiv.1505.02023>.

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.