Powerful convenience for Julia visualizations and data analysis

Automatically update function definitions in a running Julia session

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Macro(s) for vectorizing loops.

Statically sized arrays for Julia

Julia implementation of Data structures

HTTP for Julia

An experimental code analyzer for Julia. No need for additional type annotations.

Basic statistics for Julia

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

The slow descent into madness

I was gonna maintain this package, but then I got high

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

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

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

An optimized graphs package for the Julia programming language

Iterative algorithms for solving linear systems, eigensystems, and singular value problems

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

Special mathematical functions in Julia

JSON parsing and printing

Making packages work faster with more extensive precompilation

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

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Reusable array functionality for Julia's various GPU backends.

A Julia Basket of Hand-Picked Krylov Methods

An interface for tables in Julia

Julia package for Gaussian quadrature

Calculus functions in Julia

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

Mathematical functions related to statistics.

A Julia frontend, written 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.

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Krylov methods for linear problems, eigenvalues, singular values and matrix functions

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

Fast jacobian computation through sparsity exploitation and matrix coloring

Main Package for IO, loading all different kind of files

Lazy code loading for Julia

Convenient input and display of LaTeX equation strings for the Julia language