Exact Derivatives via Automatic Differentiation
Beta function for dual numbers
Composable total derivative operator
Extract the k-th derivative from a nested dual result
Extract the derivative (tangent) part of a dual number
Compute a function value and all derivatives up to order n
Create a constant dual for n-th order differentiation
Create a dual constant (derivative seed = 0)
Create a dual seeded for n-th order differentiation
Create a dual variable (derivative seed = 1)
Indexing and length for dual_vector
Dual Number Vector
Create a vector of dual numbers
Arithmetic and comparison operators for dual numbers
Two-argument arctangent for dual numbers
Coerce dual to numeric
Combine dual numbers into a dual_vector
Check if a dual number is numeric
Logarithm with optional base for dual numbers
Math group generic for dual numbers
Math2 group generic for dual numbers
Piecewise max and min for dual numbers
Display a dual number
Summary group generic for dual numbers
Create a dual number
Dual Number Class for Automatic Differentiation
Error function
Complementary error function
Compute the gradient of a scalar-valued function
Compute the Hessian of a scalar-valued function
Test whether an object is a dual number
Compute the Jacobian of a vector-valued function
Log-beta function for dual numbers
nabla: Exact Derivatives via Automatic Differentiation
Polygamma function for dual numbers
Extract the value (primal) part of a dual number
Exact automatic differentiation for R functions. Provides a composable derivative operator D that computes gradients, Hessians, Jacobians, and arbitrary-order derivative tensors at machine precision. D(D(f)) gives Hessians, D(D(D(f))) gives third-order tensors for skewness of maximum likelihood estimators, and so on to any order. Works through any R code including loops, branches, and control flow.