Julia functional programming infrastructures and metaprogramming facilities

Julia support for native CUDA programming

A data structure for mathematical optimization problems

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

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

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

A Julia package for data clustering

Special mathematical functions in Julia

Root finding functions for Julia

A Julia Basket of Hand-Picked Krylov Methods

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

A Julia package for defining and working with linear maps, also known as linear transformations or linear operators acting on vectors. The only requirement for a LinearMap is that it can act on a vector (by multiplication) efficiently.

Polynomial manipulations in Julia

An interface for tables in Julia

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

An alternative interface for dictionaries in Julia, for improved productivity and performance

A Curious Cumulation of CUDA Cuisine

Calculus functions in Julia

Named dimensions and indexing for julia arrays and other data

Julia bindings to the FFTW library for fast Fourier transforms

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

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.

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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

LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

The cheapest threads you can find!

Fast jacobian computation through sparsity exploitation and matrix coloring

Mathematical functions related to statistics.