Function reference • autodiffr

All functions
`GradientResult()` `JacobianResult()` `HessianResult()`	Create `DiffResult` Object.
`forward_deriv()` `forward_grad_config()` `forward_jacobian_config()` `forward_hessian_config()` `forward_grad()` `forward_jacobian()` `forward_hessian()`	Wrapper functions for API of `ForwardDiff.jl`.
`reverse_grad()` `reverse_jacobian()` `reverse_hessian()` `reverse_grad_config()` `reverse_jacobian_config()` `reverse_hessian_config()` `reverse_grad_tape()` `reverse_jacobian_tape()` `reverse_hessian_tape()` `reverse_compile()`	Wrapper functions for API of `ReverseDiff.jl`.
`TESTING_FUNCS`	Testing functions.
`ad_setup()`	Do initial setup for package autodiffr.
`ad_variant()`	Create Variant Functions (more) Suitable for AD.
`ad_jacobian()` `ad_hessian()` `ad_grad()` `ad_deriv()`	Calculate Gradient, Jacobian and Hessian using Automatic Differentiation.
`makeJacobianFunc()` `makeHessianFunc()` `makeGradFunc()` `makeDerivFunc()`	Create (Optimized) Gradient, Jacobian and Hessian Functions using Automatic Differentiation.
`autodiffr`	autodiffr.
`rSums()` `rMeans()` `cSums()` `cMeans()`	Row and Column Sums and Means Compatable with JuliaObject
`diagm()`	Construct a diagonal matrix
`%m%`	Matrix Multiplication Compatable with JuliaObject.
`julia_array()`	Create Julia Arrays.
`map()`	Apply a Function to Multiple List or Vector Arguments
`ones()`	Create an array of all ones with the same element type and dims as `x`.
`zeros()`	Create an array of all zeros with the same element type and dims as `x`.

Reference