anchor.diff:  
   #### Hessian of a vector-to-scalar function
   
  -Signature: `AD.Hessian(Func<D[],D>)`
  -
  -Return value: `Func<D[],D[,]>`
  -
  -For a function $f(a_1, \dots, a_n): \mathbb{R}^n \to \mathbb{R}$, this gives the [Hessian matrix](http://en.wikipedia.org/wiki/Hessian_matrix)
  +Syntax: `public static Func<D[],D[,]> AD.Hessian(Func<D[],D> f)`
  +
  +For a function $f(a_1, \dots, a_n): \mathbb{R}^n \to \mathbb{R}$, this returns a function that computes the [Hessian matrix](http://en.wikipedia.org/wiki/Hessian_matrix)
   
   $$$
     \mathbf{H}_f = \begin{bmatrix}
  
positive.code: 
  #### Gradient of a vector-to-scalar function evaluated at a point
  
  Signature: `AD.Grad(Func<D[],D>, D[])`
  
  Return value: `D[]`
  
  For a function $f(a_1, \dots, a_n): \mathbb{R}^n \to \mathbb{R}$, and $\mathbf{x} \in \mathbb{R}^n$, this gives the gradient evaluated at $\mathbf{x}$
  
  $$$
    \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}} \; .
  
negative.code: 
  #### Second derivative of a scalar-to-scalar function
  
  Syntax: `public static Func<D,D> AD.Diff2(Func<D,D> f)`
  
  For a function $f(a): \mathbb{R} \to \mathbb{R}$, this returns a function that computes the second derivative
  
  $$$
    \frac{d^2}{da^2} f(a) \; .
  