Powerful convenience for Julia visualizations and data analysis

In-memory tabular data in Julia

CUDA programming in Julia.

Optimization functions for Julia

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

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.

HDF5-compatible file format in pure Julia

Fast, continuous interpolation of discrete datasets in Julia

Utility library for working with CSV and other delimited files in the Julia programming language

An optimized graphs package for the Julia programming language

Julia bindings for the Enzyme automatic differentiator

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

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

Print data in formatted tables.

A data structure for mathematical optimization problems

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

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Reusable array functionality for Julia's various GPU backends.

🏔️Manopt. jl – Optimization on Manifolds in Julia

JSON parsing and printing

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

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.

An interface for tables in Julia

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

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.

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

Fast jacobian computation through sparsity exploitation and matrix coloring