Powerful convenience for Julia visualizations and data analysis

21st century AD

Package to call Python functions from the Julia language

Symbolic programming for the next generation of numerical software

Automatically update function definitions in a running Julia session

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.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Progress meter for long-running computations

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.

Symbolic expressions, rewriting and simplification

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

Elegant & Performant Scientific Machine Learning in Julia

Plotting for Julia based on matplotlib.pyplot

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

No-overhead breakpoints in Julia

Julia functional programming infrastructures and metaprogramming facilities

A data structure for mathematical optimization problems

Filter design, periodograms, window functions, and other digital signal processing functionality

Overdub Your Julia Code

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

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.