class_docstr: Student t Distribution
  ----
  This class contains methods relating to the Student t distribution for time series.
class_docstr_tok: ["Student","t","Distribution","--","--","This","class","contains","methods","relating","to","the","Student","t","distribution","for","time","series","."]
docstr: Parameters
  ----------
  loc : float
      Location parameter for the t distribution
  
  scale : float
      Scale parameter for the t distribution
  
  df : float
      Degrees of freedom parameter for the t distribution
  
  transform : str
      Whether to apply a transformation to the location variable - e.g. 'exp' or 'logit'
docstr_tok: ["Parameters","--","--","--","--","--","loc",":","float","Location","parameter","for","the","t","distribution","scale",":","float","Scale","parameter","for","the","t","distribution","df",":","float","Degrees","of","freedom","parameter","for","the","t","distribution","transform",":","str","Whether","to","apply","a","transformation","to","the","location","variable","-","e.g",".","'exp","'","or","'logit","'"]
code: def __init__(
      self, loc=0.0, scale=1.0, df=8.0, transform=None, **kwargs
  ):
      """
      Parameters
      ----------
      loc : float
          Location parameter for the t distribution
  
      scale : float
          Scale parameter for the t distribution
  
      df : float
          Degrees of freedom parameter for the t distribution
  
      transform : str
          Whether to apply a transformation to the location variable - e.g. 'exp' or 'logit'
      """
      super(t, self).__init__(transform)
      self.loc0 = loc
      self.scale0 = scale
      self.df0 = df
      self.covariance_prior = False
  
      self.gradient_only = kwargs.get(
          "gradient_only", False
      )  # used for GAS t models
      if self.gradient_only is True:
          self.score_function = self.first_order_score
      else:
          self.score_function = self.second_order_score
  
imports: import numpy
  import scipy.stats
  import scipy.special
  from .family import Family
  from .flat import Flat
  from .normal import Normal
  from .gas_recursions import gas_recursion_t_orderone, gas_recursion_t_ordertwo
  from .gas_recursions import gasx_recursion_t_orderone, gasx_recursion_t_ordertwo
  from .gas_recursions import gas_llev_recursion_t_orderone, gas_llev_recursion_t_ordertwo
  from .gas_recursions import gas_llt_recursion_t_orderone, gas_llt_recursion_t_ordertwo
  from .gas_recursions import gas_reg_recursion_t_orderone, gas_reg_recursion_t_ordertwo
label_desc: Use image node_id 1 to create a new t object from inherited base classes: Family with example: obj = t(loc, scale, df, transform)