4.7.9. statsmodels.tools.numdiff

numerical differentiation function, gradient, Jacobian, and Hessian

Author : josef-pkt License : BSD

4.7.9.1. Functions

approx_fprime(x, f[, epsilon, args, kwargs, ...])	Gradient of function, or Jacobian if function f returns 1d array
approx_fprime_cs(x, f[, epsilon, args, kwargs])	Calculate gradient or Jacobian with complex step derivative approximation
approx_hess(x, f[, epsilon, args, kwargs])	Calculate Hessian with finite difference derivative approximation
approx_hess1(x, f[, epsilon, args, kwargs, ...])	Calculate Hessian with finite difference derivative approximation
approx_hess2(x, f[, epsilon, args, kwargs, ...])	Calculate Hessian with finite difference derivative approximation
approx_hess3(x, f[, epsilon, args, kwargs])	Calculate Hessian with finite difference derivative approximation
approx_hess_cs(x, f[, epsilon, args, kwargs])	Calculate Hessian with complex-step derivative approximation