original_docstring: 
          Log probability mass function (PMF) for beta-binomial distribution.
          Args:
              endog: see betabinom class docstring.
              a, b (float or array): parameters of underlying Beta distribution.
          
docstring: Log probability mass function (PMF) for beta-binomial distribution.
docstring_tokens: Log probability mass function ( PMF ) for beta - binomial distribution .
code: def pmf_log(self, endog, a, b):
          '''
          Log probability mass function (PMF) for beta-binomial distribution.
          Args:
              endog: see betabinom class docstring.
              a, b (float or array): parameters of underlying Beta distribution.
          '''
          if isinstance(endog, pd.core.frame.DataFrame):
              endog = endog.values
          k, n = endog.T
          
          result = \
              np.array([np.log(np.arange(i, 1, -1)).sum() for i in n]) \
              - np.array([np.log(np.arange(i, 1, -1)).sum() for i in k]) \
              - np.array([np.log(np.arange(i, 1, -1)).sum() for i in n-k]) \
              + sp.special.betaln(k+a, n-k+b) \
              - sp.special.betaln(a, b)
              
          return result
code_tokens: def pmf_log ( self , endog , a , b ) : if isinstance ( endog , pd . core . frame . DataFrame ) : endog = endog . values k , n = endog . T result = np . array ( [ np . log ( np . arange ( i , 1 , - 1 ) ) . sum ( ) for i in n ] ) - np . array ( [ np . log ( np . arange ( i , 1 , - 1 ) ) . sum ( ) for i in k ] ) - np . array ( [ np . log ( np . arange ( i , 1 , - 1 ) ) . sum ( ) for i in n - k ] ) + sp . special . betaln ( k + a , n - k + b ) - sp . special . betaln ( a , b ) return result
short_docstring: Log probability mass function (PMF) for beta-binomial distribution.
short_docstring_tokens: Log probability mass function ( PMF ) for beta - binomial distribution .
comment: '''
          Log probability mass function (PMF) for beta-binomial distribution.
          Args:
              endog: see betabinom class docstring.
              a, b (float or array): parameters of underlying Beta distribution.
          '''