anchor.diff:  
   #### Second derivative of a scalar-to-scalar function
   
  -Signature: `AD.Diff2(Func<D,D>)`
  -
  -Return value: `Func<D,D>`
  -
  -For a function $f(a): \mathbb{R} \to \mathbb{R}$, this gives the second derivative
  +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) \; .
  
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: 
  #### Transposed Jacobian of a vector-to-vector function evaluated at a point
  
  Syntax: `public static D[,] AD.JacobianT(Func<D[],D[]> f, D[] x)`
  
  For a function $\mathbf{F}: \mathbb{R}^n \to \mathbb{R}^m$ with components $F_1 (a_1, \dots, a_n), \dots, F_m (a_1, \dots, a_n)$, and $\mathbf{x} \in \mathbb{R}^n$, this returns the $n$-by-$m$ transposed Jacobian matrix evaluated at $\mathbf{x}$
  
  $$$
    \left( \mathbf{J}_\mathbf{F}^\textrm{T} \right)_\mathbf{x} = \left. \begin{bmatrix}
  