anchor.diff:  
   $$$
     \left( \textrm{div} \, \mathbf{F} \right)_{\mathbf{x}} = \left( \nabla \cdot \mathbf{F} \right)_{\mathbf{x}} = \textrm{tr}\left( \mathbf{J}_{\mathbf{F}} \right)_{\mathbf{x}} = \left. \left( \frac{\partial F_1}{\partial a_1} + \dots + \frac{\partial F_n}{\partial a_n}\right) \right|_{\mathbf{a}\; = \; \mathbf{x}} \; .
  +
  +    [lang=csharp]
  +    // Divergence of a vector-to-vector function at a point
  +    var v = AD.Curl(x => new D[] { AD.Sin(x[0] * x[1]), x[0] - x[1], x[2] }, new D[] { 3, 2, 4 });
   
   Numerical Differentiation
   -------------------------
  
positive.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).
  
  ##### `AD.JacobianT(Func<D[],D[]>)`: Transposed Jacobian of a vector-to-vector function
  
  Return value: `Func<D[],D[,]>`
  
  
negative.code: $$$
    \left. \frac{d}{da} f(a) \right|_{a\; =\; x} \; .
  
      [lang=csharp]
      // Derivative of a scalar-to-scalar function at a point
      var v = AD.Diff(x => AD.Sin(x * x - AD.Exp(x)), 3);
  
  ### AD.Diff2
  
  #### Second derivative of a scalar-to-scalar function
  
  Signature: `AD.Diff2(Func<D,D>)`
  
  Return value: `Func<D,D>`
  
  