Update immutable data

Calculating contour curves for 2D scalar fields in Julia

Simple, consistent interfaces for any codec.

Root finding functions for Julia

Powerful convenience for Julia visualizations and data analysis

Primitives for construction of objects

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.

Forward Mode Automatic Differentiation for Julia

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

Julia implementation of Data structures

Julia macro for re-exporting one module from another

Basic statistics for Julia

A common solve function for scientific machine learning (SciML) and beyond

A Julia package to provide C and Python-like formatting support

Colorschemes, colormaps, gradients, and palettes

Fixed point types for julia

Physical quantities with arbitrary units

Reduce time-to-first-execution of Julia code

Tools for writing functions that handle many kinds of IO arguments

Package to call Python functions from the Julia language

Compatibility across Julia versions

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A statistics-focused namespace for packages to share functions

Extensions for Julia's docsystem.

HTTP for Julia

Conda managing Julia binary dependencies

JSON parsing and printing

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Generic helper algorithms for building plotting components

An enhancement to julia piping syntax

Statically sized arrays for Julia

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

Common interface for symbolic terms, allowing symbolic libraries with different underlying structures to interact and convert

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

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

Julia Package for the FFMPEG builder binaries

Themes for the Julia plotting package Plots.jl

Julia implementation of associative containers that preserve insertion order

Julia interface to SymPy via PyCall

A performant, 100% native-julia SHA1, SHA2, and SHA3 implementation