class_docstr: Base class for density estimation and regression KDE classes.
class_docstr_tok: ["Base","class","for","density","estimation","and","regression","KDE","classes","."]
docstr: Computes the measure of dispersion.
  
  The minimum of the standard deviation and interquartile range / 1.349
  
  Notes
  -----
  Reimplemented in `KernelReg`, because the first column of `data` has to
  be removed.
  
  References
  ----------
  See the user guide for the np package in R.
  In the notes on bwscaling option in npreg, npudens, npcdens there is
  a discussion on the measure of dispersion
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code: def _compute_dispersion(self, data):
      """
      Computes the measure of dispersion.
  
      The minimum of the standard deviation and interquartile range / 1.349
  
      Notes
      -----
      Reimplemented in `KernelReg`, because the first column of `data` has to
      be removed.
  
      References
      ----------
      See the user guide for the np package in R.
      In the notes on bwscaling option in npreg, npudens, npcdens there is
      a discussion on the measure of dispersion
      """
      return _compute_min_std_IQR(data)
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from .None import kernels
label_desc: Use image node_id 2 for calling the GenericKDE obj's underlying member method code with example usage: obj._compute_dispersion(data) and returns: _compute_min_std_IQR