anchor.diff:  $$$
     \frac{d^n}{da^n} f(a) \; .
   
  -##### `AD.Diffn(Int32, Func<D,D>, D)`: N-th derivative of a scalar-to-scalar function evaluated at a point
  +    [lang=csharp]
  +    // Fifth derivative of a scalar-to-scalar function
  +    var df = AD.Diffn(5, x => AD.Sin(x * x - AD.Exp(x)));
  +
  +    // Evaluate df at a point
  +    var v = df(3);
  +
  +#### N-th derivative of a scalar-to-scalar function evaluated at a point
  +
  +Signature: `AD.Diffn(Int32, Func<D,D>, D)`
   
   Return value: `D`
   
  
positive.code: $$$
    \left( \nabla f \right)_\mathbf{x} = \left. \left[ \frac{\partial f}{{\partial a}_1}, \dots, \frac{\partial f}{{\partial a}_n} \right] \right|_{\mathbf{a}\; = \; \mathbf{x}} \; .
  
  ##### `AD.Gradv(Func<D[],D>, D[], D[])`: Gradient-vector product (directional derivative)
  
  Return value: `D`
  
  
negative.code:   
  With AD, this value is computed efficiently in one forward evaluation of the function, in a matrix-free way (without computing the full Jacobian matrix).
  
      [lang=csharp]
      // Jacobian-vector product of a vector-to-vector function
      var v = AD.Jacobianv(x => new D[] { AD.Sin(x[0] * x[1]), x[0] - x[1], x[2] }, new D[] { 3, 2, 4 }, new D[] { 1, 2, 3 });
  
  ### AD.JacobianT
  
  #### Transposed Jacobian of a vector-to-vector function
  
  Signature: `AD.JacobianT(Func<D[],D[]>)`
  
  Return value: `Func<D[],D[,]>`
  
  