class_docstr: TruncatedNormal Distribution
  ----
  This class contains methods relating to the truncated normal distribution for time series.
docstr: Parameters
  ----------
  mu : float
      Mean parameter for the Truncated Normal distribution
  
  sigma : float
      Standard deviation for the Truncated Normal distribution
  
  lower: float
      Lower truncation for the distribution
  
  upper: float
      Upper truncation for the distribution
  
  transform : str
      Whether to apply a transformation - e.g. 'exp' or 'logit'
code: def __init__(
      self,
      mu=0.0,
      sigma=1.0,
      lower=None,
      upper=None,
      transform=None,
      **kwargs,
  ):
      """
      Parameters
      ----------
      mu : float
          Mean parameter for the Truncated Normal distribution
  
      sigma : float
          Standard deviation for the Truncated Normal distribution
  
      lower: float
          Lower truncation for the distribution
  
      upper: float
          Upper truncation for the distribution
  
      transform : str
          Whether to apply a transformation - e.g. 'exp' or 'logit'
      """
      super(TruncatedNormal, self).__init__(transform)
      self.mu0 = mu
      self.sigma0 = sigma
      self.upper = upper
      self.lower = lower
      self.covariance_prior = False
  
file_path: pyflux/pyflux/families/truncated_normal.py
imports: import numpy
  import scipy.stats
  import scipy.special
  from .family import Family
label_desc: Use image node_id 1 to create a new TruncatedNormal object from inherited base classes: Family with example: obj = TruncatedNormal(mu, sigma, lower, upper, transform)