Generates multivariate subgaussian stable probabilities using the QRSVN
    algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394>
    but by sampling positive stable variates not chi/sqrt(nu).

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>,
    Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by 
	Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.

Methods for estimating and utilizing the multivariate generalized
    propensity score (mvGPS) for multiple continuous exposures described in
    Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow
    estimation of a dose-response surface relating the joint distribution of multiple
    continuous exposure variables to an outcome. Weights are constructed assuming a
    multivariate normal density for the marginal and conditional distribution of
    exposures given a set of confounders. Confounders can be different for different
    exposure variables. The weights are designed to achieve balance across all
    exposure dimensions and can be used to estimate dose-response surfaces.

Tabulate and plot directional and other multivariate histograms.

Hypothesis tests for multivariate data. Tests for one and two mean vectors, multivariate analysis of variance, tests for one, two or more covariance matrices. References include: Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.