class_docstr: Base Class for multivariate elliptical distributions, normal and t
  
  contains common initialization, and some common methods
  subclass needs to implement at least rvs and logpdf methods
class_docstr_tok: ["Base","Class","for","multivariate","elliptical","distributions",",","normal","and","t","contains","common","initialization",",","and","some","common","methods","subclass","needs","to","implement","at","least","rvs","and","logpdf","methods"]
docstr: return a normalized distribution where sigma=corr
  
  if demeaned is True, then mean will be set to zero
code: def normalized(self, demeaned=True):
      """return a normalized distribution where sigma=corr
  
      if demeaned is True, then mean will be set to zero
  
      """
      if demeaned:
          mean_new = np.zeros_like(self.mean)
      else:
          mean_new = self.mean / self.std_sigma
      sigma_new = self.corr
      args = [getattr(self, ea) for ea in self.extra_args]
      return self.__class__(mean_new, sigma_new, *args)
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 11 for calling the MVElliptical obj's underlying member method code with example usage: obj.normalized(demeaned) and returns: self