def d2logpdf_dlink2_dr(self, link_f, y, Y_metadata=None):
c = np.zeros_like(y)<EOL>if Y_metadata is not None and '<STR_LIT>' in Y_metadata.keys():<EOL><INDENT>c = Y_metadata['<STR_LIT>']<EOL><DEDENT>uncensored = (<NUM_LIT:1>-c)*-<NUM_LIT:2>*y**self.r*np.log(y)/link_f**<NUM_LIT:3><EOL>censored = c*-<NUM_LIT:2>*y**self.r*np.log(y)/link_f**<NUM_LIT:3><EOL>d2logpdf_dlink_dr = uncensored + censored<EOL>return d2logpdf_dlink_dr<EOL>
Derivative of hessian of loglikelihood wrt r-shape parameter.
:param link_f:
:param y:
:param Y_metadata:
:return: