class_docstr: Apply the Box-Cox transformation to an endogenous array
  
  The Box-Cox transformation is applied to non-normal data to coerce it more
  towards a normal distribution. It's specified as::
  
      (((y + lam2) ** lam1) - 1) / lam1, if lmbda != 0, else
      log(y + lam2)
  
  Parameters
  ----------
  lmbda : float or None, optional (default=None)
      The lambda value for the Box-Cox transformation, if known. If not
      specified, it will be estimated via MLE.
  
  lmbda2 : float, optional (default=0.)
      The value to add to ``y`` to make it non-negative. If, after adding
      ``lmbda2``, there are still negative values, a ValueError will be
      raised.
  
  neg_action : str, optional (default="raise")
      How to respond if any values in ``y <= 0`` after adding ``lmbda2``.
      One of ('raise', 'warn', 'ignore'). If anything other than 'raise',
      values <= 0 will be truncated to the value of ``floor``.
  
  floor : float, optional (default=1e-16)
      A positive value that truncate values to if there are values in ``y``
      that are zero or negative and ``neg_action`` is not 'raise'. Note that
      if values are truncated, invertibility will not be preserved, and the
      transformed array may not be perfectly inverse-transformed.
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code: def __init__(
      self, lmbda=None, lmbda2=0, neg_action="raise", floor=1e-16
  ):
      self.lmbda = lmbda
      self.lmbda2 = lmbda2
      self.neg_action = neg_action
      self.floor = floor
  
file_path: pmdarima/pmdarima/preprocessing/endog/boxcox.py
imports: from scipy import stats
  import numpy
  import warnings
  from ...compat import check_is_fitted
  from .base import BaseEndogTransformer
label_desc: Use image node_id 1 to create a new BoxCoxEndogTransformer object from inherited base classes: BaseEndogTransformer with example: obj = BoxCoxEndogTransformer(lmbda, lmbda2, neg_action, floor)