class_docstr: Student Skew t Distribution
  ----
  This class contains methods relating to the Student Skew t distribution for time series.
docstr: Negative loglikelihood function
  
  Parameters
  ----------
  y : np.ndarray
      univariate time series
  
  mean : np.ndarray
      array of location parameters for the Skew t distribution
  
  scale : float
      scale parameter for the Skew t distribution
  
  shape : float
      tail thickness parameter for the Skew t distribution
  
  skewness : float
      skewness parameter for the Skew t distribution
  
  Returns
  ----------
  - Negative loglikelihood of the Skew t family
docstr_tok: ["Negative","loglikelihood","function","Parameters","--","--","--","--","--","y",":","np.ndarray","univariate","time","series","mean",":","np.ndarray","array","of","location","parameters","for","the","Skew","t","distribution","scale",":","float","scale","parameter","for","the","Skew","t","distribution","shape",":","float","tail","thickness","parameter","for","the","Skew","t","distribution","skewness",":","float","skewness","parameter","for","the","Skew","t","distribution","Returns","--","--","--","--","--","-","Negative","loglikelihood","of","the","Skew","t","family"]
code: def neg_loglikelihood(y, mean, scale, shape, skewness):
      """Negative loglikelihood function
  
      Parameters
      ----------
      y : np.ndarray
          univariate time series
  
      mean : np.ndarray
          array of location parameters for the Skew t distribution
  
      scale : float
          scale parameter for the Skew t distribution
  
      shape : float
          tail thickness parameter for the Skew t distribution
  
      skewness : float
          skewness parameter for the Skew t distribution
  
      Returns
      ----------
      - Negative loglikelihood of the Skew t family
      """
      m1 = (np.sqrt(shape) * sp.gamma((shape - 1.0) / 2.0)) / (
          np.sqrt(np.pi) * sp.gamma(shape / 2.0)
      )
      mean = mean + (skewness - (1.0 / skewness)) * scale * m1
      return -np.sum(
          Skewt.logpdf_internal(
              x=y, df=shape, loc=mean, gamma=skewness, scale=scale
          )
      )
  
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_skewt_orderone, gas_recursion_skewt_ordertwo
  from .gas_recursions import gasx_recursion_skewt_orderone, gasx_recursion_skewt_ordertwo
  from .gas_recursions import gas_llev_recursion_skewt_orderone, gas_llev_recursion_skewt_ordertwo
  from .gas_recursions import gas_llt_recursion_skewt_orderone, gas_llt_recursion_skewt_ordertwo
  from .gas_recursions import gas_reg_recursion_skewt_orderone, gas_reg_recursion_skewt_ordertwo
label_desc: Use image node_id 13 for calling the Skewt obj's underlying member method code with example usage: obj.neg_loglikelihood(y, mean, scale, shape, skewness) and returns: unknown