Powerful convenience for Julia visualizations and data analysis

21st century AD

Optimization functions for Julia

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Macro(s) for vectorizing loops.

Julia implementation of Data structures

Formatted output of timed sections in Julia

HTTP for Julia

A benchmarking framework for the Julia language

Physical quantities with arbitrary units

Basic statistics for Julia

Convert julia objects to LaTeX equations, arrays or other environments.

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

An optimized graphs package for the Julia programming language

Julia bindings for the Enzyme automatic differentiator

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

A Julia package for evaluating distances (metrics) between vectors.

Types with default field values, keyword constructors and (un-)pack macros

Julia functional programming infrastructures and metaprogramming facilities

A data structure for mathematical optimization problems

Overdub Your Julia Code

Heterogeneous programming in Julia

Plotting for Julia based on GR, a framework for visualisation applications

Special mathematical functions in Julia

Reverse Mode Automatic Differentiation for Julia

A Julia Basket of Hand-Picked Krylov Methods

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Efficient implementation of struct arrays in Julia

Reusable array functionality for Julia's various GPU backends.

JSON parsing and printing

MacroTools provides a library of tools for working with Julia code and expressions.

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Lazy arrays and linear algebra in Julia

An interface for tables in Julia

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Adaptive 1d numerical Gauss–Kronrod integration in 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.