R: Accurate Numerical Derivatives

numDeriv-package {numDeriv} | R Documentation

## 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.

[Package numDeriv version 2016.8-1 Index]