covar_theta : array, shape=(len(theta), len(theta))
Covariance matrix of theta. This is estimated by the inverse hessian
of the log likelihood function.
theta : array of shape = (n*(n-1)/2 + n) for dense or shorter
The free parameters of the model. These values are the (possibly sparse)
linearized elements of the log of the upper triangular portion of the
symmetric rate matrix, S, followed by the log of the equilibrium
distribution.
n : int
inds : array, optional (default=None)
Sparse linearized triu indices theta. If not supplied, theta is
assumed to be a dense parameterization of the upper triangular portion
of the symmetric rate matrix followed by the log equilibrium weights,
and must be of length n*(n-1)/2 + n. If inds is supplied, it is a
set of indices, with len(inds) == len(theta),
0 <= inds < n*(n-1)/2+n, giving the indices of the nonzero elements
of the upper triangular elements of the rate matrix to which
theta correspond.
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