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.
An expansion of R’s ‘stats’ random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) doi:10.1214/aos/1176325375, and pseudo wishart, Diaz-Garcia, et al.(1997) doi:10.1006/jmva.1997.1689, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) doi:10.1061/(ASCE)0733-9399(2008)134:12(1029), commonly used in volatility modeling. Users can also use this package to create random covariance matrices.