class_docstr: Running kurtosis using Welford's algorithm.
  
  Parameters
  ----------
  bias
      If `False`, then the calculations are corrected for statistical bias.
  
  Examples
  --------
  
  >>> from river import stats
  >>> import scipy.stats
  >>> import numpy as np
  
  >>> np.random.seed(42)
  >>> X = np.random.normal(loc=0, scale=1, size=10)
  
  >>> kurtosis = stats.Kurtosis(bias=False)
  >>> for x in X:
  ...     kurtosis.update(x)
  ...     print(kurtosis.get())
  -3.0
  -2.0
  -1.5
  1.4130027920707047
  0.15367976585756438
  0.46142633246812653
  -1.620647789230658
  -1.3540178492487054
  -1.2310268787102745
  -0.9490372374384453
  
  >>> for i in range(2, len(X)+1):
  ...     print(scipy.stats.kurtosis(X[:i], bias=False))
  -2.0
  -1.4999999999999998
  1.4130027920707082
  0.15367976585756082
  0.46142633246812403
  -1.620647789230658
  -1.3540178492487063
  -1.2310268787102738
  -0.9490372374384459
  
  >>> kurtosis = stats.Kurtosis(bias=True)
  >>> for x in X:
  ...     kurtosis.update(x)
  ...     print(kurtosis.get())
  -3.0
  -2.0
  -1.5
  -1.011599627723906
  -0.9615800585356089
  -0.6989395431537853
  -1.4252699121794408
  -1.311437071070812
  -1.246289111322894
  -1.082283689864171
  
  >>> for i in range(2, len(X)+1):
  ...     print(scipy.stats.kurtosis(X[:i], bias=True))
  -2.0
  -1.4999999999999998
  -1.0115996277239057
  -0.9615800585356098
  -0.6989395431537861
  -1.425269912179441
  -1.3114370710708125
  -1.2462891113228936
  -1.0822836898641714
  
  References
  ----------
  [^1]: [Wikipedia article on algorithms for calculating variance](https://www.wikiwand.com/en/Algorithms_for_calculating_variance#/Covariance)
code: def update(self, x):
      self._kurtosis.update(x)
  
file_path: river/river/stats/kurtosis.py
imports: from __future__ import annotations
  from river import stats
  from river.stats import _rust_stats
label_desc: Use image node_id 2 for calling the Kurtosis obj's underlying member method code with example usage: obj.update(x) without return types