code: def noncentral_t_like(x, mu, lam, nu):
      R
      mu = np.asarray(mu)
      lam = np.asarray(lam)
      nu = np.asarray(nu)
      return flib.nct(x, mu, lam, nu)
docs: R"""
      Non-central Student's T log-likelihood.
  
      Describes a normal variable whose precision is gamma distributed.
  
      .. math::
          f(x|\mu,\lambda,\nu) = \frac{\Gamma(\frac{\nu +
          1}{2})}{\Gamma(\frac{\nu}{2})}
          \left(\frac{\lambda}{\pi\nu}\right)^{\frac{1}{2}}
          \left[1+\frac{\lambda(x-\mu)^2}{\nu}\right]^{-\frac{\nu+1}{2}}
  
      :Parameters:
        - `x` : Input data.
        - `mu` : Location parameter.
        - `lambda` : Scale parameter.
        - `nu` : Degrees of freedom.