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
  
  
  
  second implementation for testing affine transformation
code: def normalized2(self, demeaned=True):
      """return a normalized distribution where sigma=corr
  
  
  
      second implementation for testing affine transformation
      """
      if demeaned:
          shift = -self.mean
      else:
          shift = self.mean * (1.0 / self.std_sigma - 1.0)
      return self.affine_transformed(
          shift, np.diag(1.0 / self.std_sigma)
      )
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 12 for calling the MVElliptical obj's underlying member method code with example usage: obj.normalized2(demeaned) and returns: self