A common suite of test functions for stressing the robustness of differentiation tools.

Tools to help generate operator overloads based on ChainRules

A Cassette-based automatic differentiation package for the Julia language

A package which provides an API for querying differentiation results at multiple orders simultaneously

The Documenter Theme for the ChainRules family of packages. But you can use it too

Julia implementation of HyperDualNumbers

Julia package for representing dual numbers and for performing dual algebra

An abstract interface for automatic differentiation.

Utilities for testing custom AD primitives.

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

A simple shared suite of common derivative definitions

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Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Taylor polynomial expansions in one and several independent variables.

Reverse Mode Automatic Differentiation for Julia

Next-generation AD

Fast jacobian computation through sparsity exploitation and matrix coloring

Taylor-mode automatic differentiation for higher-order derivatives

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

Forward Mode Automatic Differentiation for Julia

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.