Powerful convenience for Julia visualizations and data analysis

Package to call Python functions from the Julia language

Symbolic programming for the next generation of numerical software

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Julia implementation of Data structures

Formatted output of timed sections in Julia

HTTP for Julia

Physical quantities with arbitrary units

Basic statistics for Julia

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

Symbolic expressions, rewriting and simplification

An optimized graphs package for the Julia programming language

Julia bindings for the Enzyme automatic differentiator

Julia functional programming infrastructures and metaprogramming facilities

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

Special mathematical functions 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.

An interface for tables in Julia

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Julia interface to SymPy via PyCall

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.

Mathematical functions related to statistics.

A combinatorics library for Julia

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

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

Color manipulation utilities for Julia

Reduce time-to-first-execution of Julia code

Abstract julia interfaces for working with trees

Lazy code loading for Julia

Colorschemes, colormaps, gradients, and palettes

Julia package for lazily representing matrices filled with a single entry

One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD

Julia implementations for different rotation parameterizations

Update immutable data

Conda managing Julia binary dependencies

Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.