anchor.diff:  
   #### Transposed Jacobian of a vector-to-vector function evaluated at a point
   
  -Signature: `AD.JacobianT(Func<D[],D[]>, D[])`
  -
  -Return value: `D[,]`
  -
  -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 gives the $n$-by-$m$ transposed Jacobian matrix evaluated at $\mathbf{x}$
  +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}
  
positive.code: 
  #### N-th derivative of a scalar-to-scalar function
  
  Signature: `AD.Diffn(Int32, Func<D,D>)`
  
  Return value: `Func<D,D>`
  
  For $n \in \mathbb{N}$ and a function $f(a): \mathbb{R} \to \mathbb{R}$, this gives the n-th derivative
  
  $$$
    \frac{d^n}{da^n} f(a) \; .
  
negative.code: 
  #### Second derivative of a scalar-to-scalar function evaluated at a point
  
  Syntax: `public static D AD.Diff2(Func<D,D> f, D x)`
  
  For a function $f(a): \mathbb{R} \to \mathbb{R}$, and $x \in \mathbb{R}$, this returns the second derivative evaluated at $x$
  
  $$$
    \left. \frac{d^2}{da^2} f(a) \right|_{a\; =\; x} \; .
  