numDeriv-package: Accurate Numerical Derivatives

In numDeriv: Accurate Numerical Derivatives

Description

Calculate (accurate) numerical approximations to derivatives.

Details

The main functions are

grad	  to calculate the gradient (first derivative) of a scalar 
  	  real valued function (possibly applied to all elements 
  	  of a vector argument).

jacobian  to calculate the gradient of a real m-vector valued
  	  function with real n-vector argument.

hessian   to calculate the Hessian (second derivative) of a scalar 
  	  real valued function with real n-vector argument.

genD	  to calculate the gradient and second derivative of a
  	  real m-vector valued function with real n-vector 
	  argument.

Author(s)

Paul Gilbert, based on work by Xingqiao Liu, and Ravi Varadhan (who wrote complex-step derivative codes)

References

Linfield, G. R. and Penny, J. E. T. (1989) Microcomputers in Numerical Analysis. New York: Halsted Press.

Fornberg, B. and Sloan, D, M. (1994) “A review of pseudospectral methods for solving partial differential equations.” Acta Numerica, 3, 203-267.

Lyness, J. N. and Moler, C. B. (1967) “Numerical Differentiation of Analytic Functions.” SIAM Journal for Numerical Analysis, 4(2), 202-210.

numDeriv documentation built on June 6, 2019, 5:07 p.m.