random_covmat generates random VAR model \((dxd)\) error term covariance matrix \(\Omega\)
from (scaled) Wishart distribution.
random_covmat(d, omega_scale)the number of time series in the system.
a size \((dx1)\) strictly positive vector specifying the scale and variability of the
random covariance matrices in random mutations. The covariance matrices are drawn from (scaled) Wishart
distribution. Expected values of the random covariance matrices are diag(omega_scale). Standard
deviations of the diagonal elements are sqrt(2/d)*omega_scale[i]
and for non-diagonal elements they are sqrt(1/d*omega_scale[i]*omega_scale[j]).
Note that for d>4 this scale may need to be chosen carefully. Default in GAfit is
var(stats::ar(data[,i], order.max=10)$resid, na.rm=TRUE), i=1,...,d.
Returns \((d(d+1)/2x1)\) vector containing vech-vectorized covariance matrix \(\Omega\).