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.

Forward Mode Automatic Differentiation for Julia

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

Taylor-mode automatic differentiation for higher-order derivatives

Fast jacobian computation through sparsity exploitation and matrix coloring

Next-generation AD

Reverse Mode Automatic Differentiation for Julia

Taylor polynomial expansions in one and several independent variables.

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

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A simple shared suite of common derivative definitions

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

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

Utilities for testing custom AD primitives.

An abstract interface for automatic differentiation.

Julia package for representing dual numbers and for performing dual algebra

Julia implementation of HyperDualNumbers

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

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

A Cassette-based automatic differentiation package for the Julia language

Tools to help generate operator overloads based on ChainRules

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