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Hi, thank you for your very nice work to port this algorithm to C++.
Would it be possible to add a method to get the final approximated covariance matrix of the parameters, ie the inverse Hessian, like for instance the final_grad does for the final gradiant of the objective function ? The scipy implementation have this capability.
The text was updated successfully, but these errors were encountered:
Hi, thank you for your very nice work to port this algorithm to C++.
Would it be possible to add a method to get the final approximated covariance matrix of the parameters, ie the inverse Hessian, like for instance the final_grad does for the final gradiant of the objective function ? The scipy implementation have this capability.
The text was updated successfully, but these errors were encountered: