class_docstr: clipped continuous distribution with a masspoint at clip_lower
  
  
  Notes
  -----
  first version, to try out possible designs
  insufficient checks for valid arguments and not clear
  whether it works for distributions that have compact support
  
  clip_lower is fixed and independent of the distribution parameters.
  The clip_lower point in the pdf has to be interpreted as a mass point,
  i.e. different treatment in integration and expect function, which means
  none of the generic methods for this can be used.
  
  maybe this will be better designed as a mixture between a degenerate or
  discrete and a continuous distribution
  
  Warning: uses equality to check for clip_lower values in function
  arguments, since these are floating points, the comparison might fail
  if clip_lower values are not exactly equal.
  We could add a check whether the values are in a small neighborhood, but
  it would be expensive (need to search and check all values).
code: def __init__(self, base_dist, clip_lower):
      self.base_dist = base_dist
      self.clip_lower = clip_lower
  
file_path: statsmodels/statsmodels/sandbox/distributions/otherdist.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 1 to create a new ClippedContinuous object  with example: obj = ClippedContinuous(base_dist, clip_lower)