docstr: First derivative of the dependence function
  
  implemented through numerical differentiation
docstr_tok: ["First","derivative","of","the","dependence","function","implemented","through","numerical","differentiation"]
code: def deriv(self, t, *args):
      """First derivative of the dependence function
  
      implemented through numerical differentiation
      """
      t = np.atleast_1d(t)
      return _approx_fprime_cs_scalar(t, self.evaluate)
  
imports: import numpy
  from scipy import stats
  from statsmodels.tools.numdiff import _approx_fprime_cs_scalar, approx_hess
label_desc: Use image node_id 3 for calling the PickandDependence obj's underlying member method code with example usage: obj.deriv(t) and returns: _approx_fprime_cs_scalar